O

OARS Multidimensional Functional
Assessment Questionnaire (OMFAQ)
▶ Duke Older Americans Resources and
Services Instrument

Obesity, an Overview
John Roger Andersen
Faculty of Health Studies, Sogn og Fjordane
University College Førde Central Hospital,
Førde, Norway

Definition
Obesity is defined as a body mass index (BMI)

30 related to excess fat storage. Class I obesity
is defined as a BMI 30–34.9, class II obesity
as a BMI 35.0–39.9, and class III obesity as
a BMI 
40. Severe obesity is defined as a
BMI 
40 or 
35 with obesity-related
comorbidities (WHO, 2000).

Description
This entry is about severe obesity (SO), as this
condition is especially associated with impairments in many aspects of quality of life (QoL).
There is however large variation in QoL within
individuals with SO. Patients with SO who seek

bariatric surgery often tend to have poorer QoL,
than those seeking a lifestyle intervention
programs or no treatment at all (Kolotkin,
Meter, & Williams, 2001).
The Physical Dimension
Most subjects with SO have some kind of physical
limitations (Dixon, Dixon, & O’Brien, 2001).
Many patients suffer from chronic pains in the
joints and/or muscles, making walking difficult.
Sometimes the physical limitations can be quite
severe, leading to loss of ability to sufficient
self-care. The physical limitations of a person
with obesity are considered to relate strongly to
the degree of personal tolerance of excess weight.
Thus, some persons may have severe physical
problems at a BMI of 35, while others first will
have the same kind of problems at a BMI of 45.
The Mental Dimension
Mental health problems are more prevalent in
subjects with severe obesity than in the general
population, and about 50 % of all patients who
seek obesity surgery seem to have some sort of
mental problems (Sullivan, Karlsson, Sjöström,
& Taft, 2001). Interestingly, there seems to be
a link between the degree of limitations in
physical functioning, pain, and mental problems.
Thus, mental problems related to severe obesity
often occur in those with the poorest physical
health (Andersen et al., 2010). Although there
are no reasons to doubt that reciprocity exists
between obesity, physical limitations, and mental
health, depressive symptomatology has been

A.C. Michalos (ed.), Encyclopedia of Quality of Life and Well-Being Research,
DOI 10.1007/978-94-007-0753-5, # Springer Science+Business Media Dordrecht 2014

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reported to flow mainly from poor physical health
to poor mental health rather than in the reverse
direction (Gayman, Turner, & Cui, 2008).
The Social Dimension
The negative impact of obesity on social
functioning is very common and can be
considerable (Kolotkin et al., 2001). Several
mechanisms might explain this. First, impaired
physical health might be a real barrier since it
can limit the ability to being physically active
with others. Second, patients seeking bariatric
surgery have, as mentioned, more mental problems. This may increase the risk for social isolation. Furthermore, it is also a sad fact that
discrimination against obese individuals is
a very real problem. Discrimination occurs in
key areas in our society, even inside the health
services (Puhl & Brownell, 2001). Thus, this is
something health professionals should bear in
mind when providing care and treatment to
patients with severe obesity.
The Economic Dimension
Persons who seek bariatric surgery may have an
increased risk for not having a paid job.
A systematic review has shown a J-shaped relationship between BMI and the risk for disability
pension (Neovius, Johansson, Rossner, &
Neovius, 2008). This is often directly related to
the effect severe obesity has on health. However,
a comprehensive literature review has also
described numerous work-related stereotypes
reported in over a dozen laboratory studies
(Puhl & Brownell, 2001). These stereotypes
could affect wages, promotion and termination,
and finally participation in paid work.
Quality of Life After Treatment for Severe
Obesity
Main goals in the treatment of severe obesity is to
reduce the excess weight 
50 % and avoid inducing severe complications or side effects. The excess
weight starts at a BMI >25. Thus, if a patient has an
initial BMI of 50, the threshold for successes is
a BMI below 37.5. Such weight loss, or even
smaller weight loss, has been associated with
large improvements in all domains of QoL, as

Obesity, an Overview

long as the side effects are tolerable (Karlsson,
Taft, Ryden, Sjöström, & Sullivan, 2007).
Several methods can be applied in the treatment of morbid obesity, including diet, physical
activity, behavioral therapy, pharmacotherapy,
and bariatric surgery. If nonsurgical strategies
are combined, weight loss may be improved in
some patients, but convincing long-term data
after nonsurgical interventions in patients with
severe obesity are lacking (Colquitt, Picot,
Loveman, & Clegg, 2009). It is important to
acknowledge that sustained weight loss after
nonsurgical interventions is a hard task for
patients with severe obesity, due to their strong
genetic predisposition for obesity. Thus, only
bariatric surgery has demonstrated sufficient and
sustained long-term weight loss in this patient
group (Colquitt et al.). There are several prospective and cross-sectional studies that have shown
good QoL after different types of bariatric surgery (Karlsson et al., 2007; Marinari et al., 2004;
de Zwaan et al., 2002). However, some studies
have shown no or negative changes in the mental
domains (van Hout, Boekestein, Fortuin, Pelle, &
van Heck, 2006). Insufficient long-term weight
loss occurs in approximately 30 % of patients
and is associated with dissatisfaction from the
patient’s point of view (Biron et al., 2004).
A limitation in this field is the scarcity of longterm studies (>5 years of follow-up). However,
some studies suggest that a relatively good QoL
can be maintained as long as 10–25 years after
bariatric surgery (Våge, Solhaug, Viste,
Bergsholm, & Wahl, 2003; Karlsson et al.,
2007). Most patients in these studies had a BMI
between 30 and 35 at follow-up.
Coping with Life After Major Weight Loss
Qualitative research has given novel insight in
the lives of those who have lost large amounts
of weight (Bocchieri, Meana, & Fisher, 2002). It
has been shown that large weight loss may induce
some potential tension areas that must be coped
properly with to maintain a good QoL.
This includes changes in self-concept due to the
radically change in outlook and changes in
social relations and skill acquisitions like eating
behavior and social skills.

Objective and Subjective Deprivation

Conclusion
Severe obesity is a strong risk factor for a poor
QoL. If weight loss is successful, QoL usually
improves radically. However, large weight loss
may also induce life changes that must be coped
with. Thus, patients with severe obesity often
need lifelong support.

References
Andersen, J. R., Aasprang, A., Bergsholm, P., Sletteskog, N.,
Vage, V., & Natvig, G. K. (2010). Anxiety and
depression in association with morbid obesity: Changes
with improved physical health after duodenal switch.
Health and Quality of Life Outcomes, 8, 52.
Biron, S., Hould, F. S., Lebel, S., Marceau, S., Lescelleur, O.,
Simard, S., et al. (2004). Twenty years of biliopancreatic
diversion: What is the goal of the surgery? Obesity
Surgery, 14, 160–164.
Bocchieri, L. E., Meana, M., & Fisher, B. L. (2002).
Perceived psychosocial outcomes of gastric bypass
surgery: A qualitative study. Obesity Surgery, 12,
781–788.
Colquitt, J. L., Picot, J., Loveman, E., & Clegg, A. J.
(2009). Surgery for obesity. Cochrane Database System Review, CD003641.
de Zwaan, M., Lancaster, K. L., Mitchell, J. E., Howell,
L. M., Monson, N., Roerig, J. L., et al. (2002). Healthrelated quality of life in morbidly obese patients:
Effect of gastric bypass surgery. Obesity Surgery, 12,
773–780.
Dixon, J. B., Dixon, M. E., & O’Brien, P. E. (2001).
Quality of life after lap-band placement: Influence of
time, weight loss, and comorbidities. Obesity
Research, 9, 713–721.
Gayman, M. D., Turner, R. J., & Cui, M. (2008). Physical
limitations and depressive symptoms: Exploring the
nature of the association. Gerontology Series B: Psychological Sciences and Social Sciences, 63, 219–228.
Karlsson, J., Taft, C., Ryden, A., Sjöström, L., & Sullivan, M.
(2007). Ten-year trends in health-related quality of life
after surgical and conventional treatment for severe
obesity: The SOS intervention study. International
Journal of Obesity, 31, 1248–1261.
Kolotkin, R. L., Meter, K., & Williams, G. R. (2001).
Quality of life and obesity. Obesity Review, 2,
219–229.
Marinari, G. M., Murelli, F., Camerini, G., Papadia, F.,
Carlini, F., Stabilini, C., et al. (2004). A 15-year evaluation of biliopancreatic diversion according to the
Bariatric Analysis Reporting Outcome System
(BAROS). Obesity Surgery, 14, 325–328.
Neovius, K., Johansson, K., Rossner, S., & Neovius, M.
(2008). Disability pension, employment and obesity
status: A systematic review. Obesity Review, 6,
572–581.

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Puhl, R., & Brownell, K. D. (2001). Bias, discrimination,
and obesity. Obesity Research, 9, 788–805.
Sullivan, M., Karlsson, J., Sjöström, L., & Taft, C. (2001).
Why quality of life measures should be used in the
treatment of patients with obesity. In P. Björntorp
(Ed.), International textbook of obesity. Chichester,
UK: John Wiley & Sons.
Våge, V., Solhaug, J. H., Viste, A., Bergsholm, P., &
Wahl, A. K. (2003). Anxiety, depression and healthrelated quality of life after jejunoileal bypass: A 25year follow-up study of 20 female patients. Obesity
Surgery, 13, 706–713.
van Hout, G. C., Boekestein, P., Fortuin, F. A., Pelle, A. J., &
van Heck, G. L. (2006). Psychosocial functioning following bariatric surgery. Obesity Surgery, 16, 787–794.
WHO. (2000). Obesity: preventing and managing the
global epidemic. Geneva: WHO.

Object of Art
▶ Arts and Quality of Life

Objectified Body Consciousness
▶ Self-Worth and Measures of Body Image

O
Objective and Subjective
Deprivation
Ruud Muffels
Faculty of Social and Behavioural Sciences,
Tilburg University, Tilburg, The Netherlands

Synonyms
Deprivation poverty; Income deprivation; Material deprivation; Multidimensional deprivation;
Multiple disadvantage; Resources deprivation

Definition
Objective deprivation refers to the enforced lack
of basic needs or necessities in life. Subjective
deprivation refers to the perception of

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disadvantage or the feelings of deprivation.
Being objectively deprived of a good or service
many other people in society or their peers have
(basic needs and status goods) causes someone to
feel more deprived than when someone lacks
a good that only few people have (luxury
goods). Lacking caviar for dinner lets someone
feel less deprived than lacking food necessary for
not being hungry every day. Deprivation is necessarily a multidimensional concept since it
refers to a variety of things or resources in life
from which one might be or feel deprived of such
as income, consumption, lifestyle, functions,
capabilities, human rights, health, housing, education, employment, and social participation.
Attempts in the rich literature to define and calculate a multidimensional deprivation index
require therefore the aggregation over and the
weighing of these various dimensions and items
which raises a number of methodological issues.
The available evidence on the relationship
between deprivation and subjective well-being
show a negative relation between the two.

Description
Objective and Subjective Relative
Deprivation
As Nolan and Whelan (2011) rightly point out,
▶ deprivation has always been central to the definition of poverty in developing countries and
to notions of ▶ income poverty and ▶ social
exclusion in more developed nations in the
West. The underlying rationale might be found
in the famous controversy between Sen and
Townsend about the absolute versus relative
nature of poverty (cf. Sen, 1983; Townsend,
1979, 1985). According to Sen, any definition of
poverty must be able to assess to what extent
people’s standard of living is below a more or
less objectively defined absolute minimum standard in terms of “basic needs” or nutritional
needs for physical functioning and survival.
At the same time, he argued that how much
money you need to afford these absolute “basic
needs” is also dependent on how much money
others have especially when “basic needs” are

Objective and Subjective Deprivation

scarce as is the case in developing countries.
If you need 1,500 cal per day and this need can
only be fulfilled with rice but rice is scarce and
there is not enough for everyone to provide the
minimum amount of calories, the amount you can
afford depend on the money you have as well as
the money others have. In Townsend (1979) view
however, poverty in developed countries is
completely relative since most people in the
Western world have sufficient means to survive
and to allow a decent standard of living but lack
the material resources to afford the goods and
things in life that are needed to be able to participate effectively in the ordinary life of the society
in which one lives (see also Goodin, Heady,
Muffels, & Dirven, 1999). The implicit idea that
effective ▶ social participation is not a matter of
money alone but involves a number of other
things in life which are considered important for
a “good life” such as education, good health,
family, friends, children, and voice has
paved the way for defining measures of relative
deprivation which entail a broader range of
resources than income or consumption alone
(Runciman, 1966; Mack & Lansley, 1985).
Runciman’s definition departs from a subjective
interpretation of the deprivation concept.
According to him people compare their own living standard as measured by the possession of
particular goods with that of other people in
their reference group. People feel deprived
when they want a good that others have but that
they themselves cannot afford. In Runciman’s
subjective interpretation, ▶ social comparison is
a very important phenomenon to understand the
prevalence of subjective deprivation in society.
Income Deprivation Indices
In Sen’s approach, a more objective interpretation is used especially in his work on the
construction of an income deprivation index
(Muffels & Headey, 2012; Sen, 1983). In the
income distribution literature, the focus has
been on defining income ▶ inequality and
income deprivation indices (cf. Berrebi & Silber,
1985; Hagenaars, 1986; Yitzhaki, 1979). Berrebi
and Silber (1985) have shown that all income
inequality indices can be rewritten as

Objective and Subjective Deprivation

a weighted sum of individual deprivation coefficients being the difference between the share of
people with higher and lower incomes weighted
with the share of the own individual income. The
relationship of this measure of objective income
deprivation (sum of the income gap with richer
people) with subjective well-being has been studied by D’Ambrosio and Frick (2007) and Bellani
and D’Ambrosio (2011), showing a negative correlation after controlling for income, marital status age, and employment status.
A Relative Deprivation Index
Muffels and Fouarge (2004) used Runciman’s
subjective deprivation concept to define and
calculate a so-called deprivation index. The
deprivation index they calculated is a summary
measure of deprivation based on the information
on deprivation as contained in the European
Community Household Panel survey that
ran from 1994 to 2001, covering 14 European
countries. They have called their deprivation
index the resources deprivation index (RDI)
because it was based on a broad list of deprivation
items derived from especially Mack and
Lansley’s work in the early 1980s. These deprivation items refer to the possession of a number
of consumption durables, housing amenities,
environmental quality, and things people can or
cannot afford and do such as eating with friends.
The RDI is calculated as the weighted sum of the
item deprivation scores (0 or 1) weighted with the
proportion of haves of that particular item in
society. The way the RDI is calculated makes
it a subjective deprivation index. A similar
approach is followed by Guio, Fusco, and Marlier
(2009). Others have used different weighting
schemes and a different selection of items, e.g.,
including only items dealing with material deprivation (Layte, Nolan, Whelan, & Maı̂tre, 2001;
Whelan, Layte, & Maitre, 2002). These indices
generally aim at measuring objective material
deprivation instead. In the framework of EU
social policy making, in particular concerning
the policies to prevent and combat social exclusion, the deprivation indices methodology is
embraced by the Commission to define and
measure the rather ambiguous concept of social

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exclusion. The list of deprivation items as
contained in the ECHP is in a modified form
and also included in it its successor, the EU-SILC
data which started in 2003 with a limited number
of countries but gradually extended to cover most
of the EU member states in 2010. In 2009, under
the French presidency, a commission led by some
Nobel laureates published a report on the
measurement of economic and social progress
in which they break a lance for the use of subjective well-being measures supplementary to GDP
per capita measures (Stiglitz, Sen, & Fitoussi,
2009). In this report Sen’s related capability
framework has been used to derive indicators
for measuring economic progress.
Sen’s Capability Approach
In his later work, Sen has developed a so-called
capability approach (CA) in which capabilities
are considered important to create freedom of
choice to people to achieve the things in life
which one has reason to value most for his or
her personal life (Sen, 1993, 1999). For Sen these
capabilities determine in the end someone’s
personal well-being. In Sen’s vocabulary these
freedoms are the capabilities to achieve particular
functionings (the doings and beings) such as finding a partner, getting a job, or following
education. Capabilities are in Sen’s world the
real freedom people have or the opportunities
and choices available to them. Central is the
notion of “freedom of choice” (Alkire, 2007;
Muffels, Tsakloglou, & Mayes, 2002; Nussbaum,
1997). People make choices during their life,
contingent on their capabilities which in the end
impact their well-being outcomes. Sen’s model
implicitly has a behavioral component while it
presumes that people strive for “freedom,”
making them happier the more their efforts and
choices allow them to do the things they value
most. In a recent test of Sen’s capability framework on German and British data, Muffels and
Headey (2012) developed an empirical model for
examining the relationship between capabilities
(indicated by human capital, social capital,
cultural capital, and psychological capital),
people’s choices and the events they encounter,
and well-being outcomes. They showed the

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relevance of the capability approach for
explaining differences in subjective well-being.
Sen’s capability approach might provide a new
avenue for research into the prevalence and distribution of multiple advantage and disadvantage
in society and the effects on different measures of
objective and subjective well-being.
Discussion
The notion of deprivation has been translated into
various measures of objective and subjective
deprivation which has been used in the past by
the European commission to define a number of
indicators to monitor the development of poverty
and social exclusion in the EU Member States. In
the meantime, new approaches have been developed in the tradition of the deprivation methodology but also beyond those in Sen’s capability
framework. These approaches might provide
new avenues for research and development of
indicators in the social domain and in the domain
of measuring social and economic progress
including the development of indicators on the
environmental dimension of quality of life.
They supplement the information as contained
in traditional measures of economic and social
progress with new objective and subjective wellbeing-oriented measures. Innovative ideas and
research is needed to make this effort
academically and politically a success.

Cross-References
▶ Deprivation
▶ Inequality
▶ Poverty
▶ Social Comparison
▶ Social Exclusion
▶ Social Indicators

References
Alkire, S. (2007). Choosing dimensions: The capability
approach and multidimensional. Poverty. In
Kakwani, N. & Silber, J. (Ed.), (2008). The many
dimensions of poverty. New York: Palgrave.

Objective and Subjective Deprivation
Bellani, L., & D’Ambrosio, C. (2011). Deprivation. Social
Exclusion and Subjective Well-Being, Social Indicator
Research, 104, 67–86.
Berrebi, Z. M., & Silber, J. (1985). Income inequality
indices and deprivation: A generalization. Quarterly
Journal of Economics, 100(3), 807–881.
D’Ambrosio, C., & Frick, J. R. (2007). Income satisfaction and relative deprivation: An empirical link. Social
Indicators Research, 81, 497–519.
Goodin, R. E., Heady, B., Muffels, R., & Dirven, H.-J.
(1999). The real worlds of welfare capitalism. Cambridge: Cambridge University Press.
Guio, A. C., Fusco A., & Marlier E. (2009). An EU
approach to material deprivation using EU-SILC and
eurobarometer data. IRISS Working Paper 2009–19.
Hagenaars, A. (1986), The perception of poverty, Amsterdam-North-Holland.
Layte, R., Nolan, B., Whelan, C. T., & Maı̂tre, B. (2001).
Persistent and consistent poverty in the 1994 and 1995
waves of the European community household panel.
Review of Income and Wealth, 17, 427–450.
Mack, J., & Lansley, S. (1985). Poor Britain. London:
Allen and Unwin.
Muffels, R., & Fouarge, D. (2004). The role of European
welfare states in explaining resources deprivation.
Social Indicators Research, 68(3), 299–330.
Muffels, R. J. A., & Headey, B. (2012). Capabilities and
choices: Do they make Sen’se for understanding
objective and subjective well-being? An empirical
test of Sen’s capability framework on German and
British panel data. Social Indicators Research.
doi:10.1007/s11205-011-9978-3.
Muffels, R., Tsakloglou, P., & Mayes, D. G. (2002). Social
exclusion in European welfare states (Series on
Globalization and welfare, p. 366). Cheltenham:
Edward Elgar.
Nolan, B., & Whelan, C. T. (2011). Poverty and deprivation in Europe. Oxford/New York: Oxford University
Press.
Nussbaum, M. C. (1997). Capabilities and human rights.
Fordham Law Review, 66, 273–300.
Runciman, W. (1966), Relative deprivation and social
justice, London: Routledge and Kegan Paul.
Sen, A. (1983). Poor, relatively speaking. Oxford
Economic Papers, 35, 153–169.
Sen, A. K. (1993). Capability and well-being. In
M. Nussbaum & A. K. Sen (Eds.), The quality of life.
Oxford: Oxford University Press.
Sen, A. (1999). Commodities and capabilities. Oxford:
Oxford University Press.
Stiglitz, J., Sen, A., & Fitoussi, J. P. (2009). Report by the
commission on the measurement of economic performance and social progress. Brussels: European Union.
Townsend, P. (1979). Poverty in the United Kingdom.
Harmondsworth, UK: Penguin.
Townsend, P. (1985). A sociological approach to the
measurement of poverty–a rejoinder to professor
Amartya Sen. Oxford Economic Papers, New Series,
37(4), 659–668.

Objective and Subjective Indicators Correlations in Taiwan
Whelan, C. T., Layte, R., & Maitre, B. (2002). Persistent
deprivation in the European Union. Journal of Applied
Social Science Studies, 122, 31–54.
Yitzhaki, S. (1979). Relative deprivation and the Gini
coefficient. Quarterly Journal of Economics, 93,
321–324.

Objective and Subjective Indicators
Correlations in Taiwan
Pei-Shan Liao
Center for Survey Research, RCHSS, Academia
Sinica, Taipei, Taiwan

Synonyms
Objective and subjective life quality

Definition
An objective dimension of ▶ quality of life usually considers socioeconomic indices in a place,
which can be used to assess the level of living and
to portray the achievement or failure in certain
domains of quality of life for a place. ▶ Social
indicators are used to monitor ▶ progress toward
goals and are often incorporated into
a ▶ performance evaluation model of the social
system. The approach of using socioeconomic
indicators to measure quality of life does not
attempt to conceptually define quality of life,
per se, but incorporates attributes of ▶ social
well-being into a set of statistics or social models.
These socioeconomic indicators are viewed
as “hard” or objective indicators and include
factors associated with individual status, equality, living conditions, economic status, ▶ education, ▶ health, ▶ welfare, and government
performance.
A subjective dimension, on the other hand,
says that an individual’s cognitive perception or
satisfaction with a place reveals the subjective
evaluation of the life experience. Such a perspective is an orientation that focuses on the problem
of defining quality of life in terms of quantifiable

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categories that measure various perceptions of
human life conditions. From this perspective,
the measures of quality of life are often viewed
as “soft” or ▶ subjective indicators. The components of quality of life mainly focus on overall
satisfaction and ▶ happiness, as well as specific
life domains including work, neighborhood, local
services, environment, personal health, crime
rate and ▶ security, government performance,
and environmental quality.

Description
Previous studies have examined the association
between objective and subjective measures of
quality of life. Some studies claimed no significant effects of the former on the latter, while
others have found that improved socioeconomic
conditions contributed to higher quality of life
(Bradshaw & Fraser, 1989; Kao & Liu, 1984).
Despite their close relationship, it is unclear
whether differences among local communities
and regions in one dimension are parallel to
those in the other. For example, research has
found that people living in economically disadvantaged communities did not necessarily indicate lower satisfaction than those who lived in
more advantaged communities (see Lewis &
Lyo, 1986; Schuessler & Fisher, 1985).
These findings imply that no consistent pattern
can be found by using either objective socioeconomic indicators or subjective perceptions with
regard to the differences in quality of life among
places. This raises a question: Are objective differences in quality of life among areas at various
urbanization levels parallel to subjective differences in perceptions? Or, on the contrary, is there
generally a discrepancy between objective and
subjective quality of life indices among areas?
Such consistency or discrepancy is examined by
using data from Taiwan.
Theories explaining the association between
objective and subjective quality of life have
included perspectives from social psychological,
economic, and ecological approaches (Schuessler &
Fisher, 1985). Needs theory and ▶ comparison
theory are discussed here since these theoretical

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approaches have been employed to examine the
mutual dependence of objective and subjective
quality of life (Hagerty, 1999).
Needs theory (Diener & Lucas, 2000; Diener,
Oishi, & Lucas, 2003; Schyns, 1998), developed
by Maslow, focuses on absolute quality of life
rather than on relative differences. It is also
known as ▶ livability theory (Veenhoven, 1995)
for the “livability of one’s society is the degree to
which collective provisions and demands fit with
individual needs and capacities” (p. 36). Needs
theory asserts that there are universal human
needs and subjective appreciation of life depends
on the objective living conditions (Diener &
Lucas, 2000). This approach aims to reveal the
direct contribution of objective conditions to the
individual’s subjective perceptions where better
quality of life can be obtained by improvement
in social-economic conditions. However, people’s
perception of and satisfaction with life in socioeconomically disadvantaged communities is not
necessarily lower than those in advantaged communities (see Lewis & Lyo, 1986; Schuessler &
Fisher, 1985). In this regard, needs theory fails to
account for the differences in quality of life among
places in various forms of needs fulfillment.
Unlike needs theory which focuses on the
absolute quality of life standards, comparison
theory assumed that people make judgments
based on relative values (Hagerty, 1999, 2000;
Veenhoven, 1995). It is argued that objective
conditions affect subjective perceptions indirectly through comparisons with other possible
alternatives (Diener & Lucas, 2000). The individual’s own experience in the past or other people’s
is used as a reference group for comparisons,
although it is not always possible to identify
what the comparison base/group may be
(Hagerty, 2000). For the comparison of objective
conditions, researchers have suggested contextual analysis on indicators of social, economic,
environmental, demographic, and medical services domains (Wu & Shih, 1996). A specific
area or community within the sampling frame
can be selected and used as the reference. When
comparing subjective perceptions of life satisfaction, however, it is difficult to determine such
a reference. The main reason is the nature of the

Objective and Subjective Indicators Correlations in Taiwan

reference, whether sociopsychological factors or
certain reference persons, is unknown.
From the perspective of comparison theory,
the evaluation of quality of life or satisfaction is
based on a process of mental calculus in which
the standards of comparison are presumed to be
adjustable rather than fixed (Veenhoven, 1995).
Whether an individual feels satisfied or not is
closely associated with one’s standards of comparison (Campbell, Converse, & Rodgers, 1976;
Diener & Lucas, 2000). While objective indicators may reveal significant discrepancies among
places, subjective perceptions and satisfactions
among their residents may be the same
(Veenhoven, 1996). Therefore, the pattern of
objective indicators in quality of life may differ
from that of subjective perceptions. In order to
explore the consistency/difference between
objective indicators and subjective perceptions
of quality of life, socioeconomic indicators and
survey data from the same cities and counties
within Taiwan are used to represent objective
and subjective dimensions, respectively, of quality of life.
Secondary aggregated data for objective indicators and subjective perceptions were employed
for the purpose of analysis. Survey data on Living
Conditions of Citizens in Taiwan in the year 2000
were used to analyze the individual’s satisfaction
with various aspects of life. A total of 4,070
completed surveys were obtained; the number
of respondents ranged from 138 to 403 for each
of the 23 separate administrative areas, whether
cities or counties. Domains of life quality studied
included medical services, domestic finances,
work, education, leisure, public safety, and environmental quality. Measures of subjective life
quality were satisfaction with each of the life
domains mentioned above, with a range from
1 ¼ very dissatisfied to 4 ¼ very satisfied.
Objective indicators of quality of life were
obtained from the 2000 Statistical Yearbook of
Hsiens (Counties) and Municipalities in the
Taiwan area (excluding the islands of Kinmen
and Matsu due to unavailability of data)
(Executive Yuan, 2000). Statistics for 23 cities
and counties in Taiwan were compiled for the
same quality of life domains. Among all of the

Objective and Subjective Indicators Correlations in Taiwan

available data for the 23 cities and counties,
indicators that may reveal the functional
features and/or have previously been used as
representatives for each of the domains were
selected. These indicators were used as or were
integrated into one index for each domain. Table 1
presents descriptions of these objective and
subjective indicators, as well as their mean values
and standard deviations (SD).
With regard to the objective conditions, the
number of indicators for each domain ranged
from one to five. For comparison purposes,
some standardized Z-scores were used instead
of the actual numbers. Given the various number
of indicators, ▶ factor analysis was employed for
each of the domains, except for the indicator of
Leisure, which was with merely one indicator, in
order to obtain a domain index and confirm the
correlation among the indicators. One factor was
extracted for each of the domains, and the results
are reported in Table 2.
With respect to the subjective perceptions,
mean scores of subjective satisfaction for each
domain and for each population sample within
each city or county were calculated as the index.
In order to compare the consistency between
objective and subjective rankings in each
domain, the Spearman rank correlation coefficient (Spearman’s rho) and Kendall’s tau-b coefficient were computed.
With regard to the objective dimension of
quality of life, various patterns were observed in
different domains. For example, Taipei City, the
capital city, ranked higher in medical services
and domestic finances but low in work. On the
other hand, Hsinchu County ranked lower in both
medical services and work but high in domestic
finances. Still others, such as Hualien County,
had similar rankings across life domains. Factor
scores were used for further comparison with
subjective indices.
Little significant difference in subjective satisfaction was found among cities and counties
(Table 3). For satisfaction with medical services,
domestic finances, work, and leisure, residents in
two or more cities/counties were found to report
similar satisfaction levels with these domains
when compared to those in other cities and

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counties. These cities or counties are denoted by
“n” in Table 3, meaning no significant differences
from others. On the other hand, satisfaction with
education, public safety, and environmental quality were significantly varied across cities and
counties. Eta squares that indicate the degree of
the association between the independent and the
dependent variable, in this study quality of life
domain scores and county/city, respectively, are
reported at the bottom of Table 3. The values of
eta square ranged from .008 to .028, suggesting
that the proportions of variance in the dependent
variable that is explained by differences among
groups were not high.
In order to further explore the consistency or
discrepancy of rankings between objective and
subjective life quality among 23 cities and
counties, Kendall’s tau-b and Spearman’s rho
correlation coefficients were calculated. As indicated in Table 4, rankings based on the numerical
values of objective and subjective life quality
were significantly different in education and in
environmental quality, surprisingly with opposite
directions. Both of the correlation coefficients
were negative in education, indicating that a
city/county with a higher ranking for objective
conditions (average level of literacy and
education) was ranked low in subjective
satisfaction with educational conditions and vice
versa. On the other hand, the correlation
coefficients in environmental quality were both
positive. A city/county that was ranked higher for
objective conditions was perceived to have better
environmental quality as well. Comparisons on
the domains of medical services, domestic
finances, work, leisure, and public safety did not
have significant differences between objective
and subjective rankings of quality of life. In
other words, objective conditions on these domains
were found to not correspond to subjective
perceptions among 23 cities and counties.
In sum, mixed results were found in a further
analysis of the parallel correspondence between
objective and subjective rankings based on the
numerical values. In particular, cities or counties
with higher objective rankings in education had
lower subjective rankings, while consistent rankings were found in environmental quality. No

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Objective and Subjective Indicators Correlations in Taiwan

Objective and Subjective Indicators Correlations in Taiwan, Table 1 Description of subjective and objective
quality of life indicators
Domain
Medical
service

Domestic
finances

Work

Education

Leisure

Public safety

Type
Obj.
(N ¼ 23)

Indicator
Number of institutions for medical services

Mean (S.D.) Unit of measure
785.22
Number
(702.35)
Medical personnel per 10,000 population
70.64 (28.17) Number per
10,000 persons
Number of hospital beds
5487.83
Number
(4494.88)
Sub.
Level of satisfaction with medical facilities available 2.63 (.65)
Score 1–4
(N ¼ 3,711) in current life
Obj.
Average annual normal expenditures per household 787,058.78
$
(N ¼ 23)
(166,064.92)
Average annual normal income per household
1053,315.13 $
(200,837.54)
Average disposable income per household
832,885.96
$
(145,528.62)
Average purchases per household
605,198.43
$
(121,336.46)
Average annual disposable income per person
232,617.86
$
(35,110.65)
Sub.
Level of satisfaction with financial condition in
2.62 (.65)
Score 1–4
(N ¼ 3,915) current life
Obj.
Labor force participation rate
57.31 (3.32) %
(N ¼ 23)
Unemployment rate
3.01 (.60)
%
Sub.
Level of satisfaction with work condition in current 2.77 (.60)
Score 1–4
(N ¼ 2,760) life
Obj.
Literacy rate for population over 15 years of age
95.32 (1.86) %
(N ¼ 23)
Higher education rate for population over 15 years
20.81 (8.10) %
of age
Sub.
Level of satisfaction with current educational system 2.77 (.69)
Score 1–4
(N ¼ 3,483)
Obj.
Area (per person) of public parks, green open space, 3.52 (3.27)
Square meters
(N ¼ 23)
children’s recreation, or athletic fields within the city
per person
planning region
Sub.
Level of satisfaction with current leisure life
2.68 (.62)
Score 1–4
(N ¼ 3,738)
Obj.
Number of firefighters per 100,000 persons
34.51 (15.05) Number per
(N ¼ 23)
100,000
persons
Number of fire trucks per 10,000 persons
0.97 (.31)
Number per
10,000 persons
Death rate of accident
56.51 (20.40) Number per
100,000
persons
Sub.
Level of satisfaction with current public safety
2.15 (.66)
Score 1–4
(N ¼ 3,667)
(continued)

Objective and Subjective Indicators Correlations in Taiwan

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Objective and Subjective Indicators Correlations in Taiwan, Table 1 (continued)
Domain
Type
Environmental Obj.
quality
(N ¼ 23)

Indicator
Weight of garbage transported daily
Monthly dust fall

Mean (S.D.)
935.56
(836.37)
94.26 (26.69)

Atmospheric ozone concentration

5.18 (3.00)

Sub.
Level of satisfaction with current residential
(N ¼ 4,019) environment

2.76 (.64)

Unit of measure
Metric tons per
day
Metric tons per
square km.
Parts per
million
Score 1–4

Source 1. 2000 Statistical Yearbook of Hsiens and Municipalities, Executive Yuan, R.O.C. (Taiwan)
2. 2000 Survey on Living Conditions of Citizens in Taiwan, Executive Yuan, R.O.C. (Taiwan)

Objective and Subjective Indicators Correlations in Taiwan, Table 2 Factor analysis of objective indicators of
quality of life
Domain
Medical service

Domestic finances

Work
Education
Leisure

Public safety

Environmental quality

Objective indicator
Number of institutions for medical services
Medical personnel per 10,000 population
Number of hospital beds
Average annual normal expenditures per household
Average annual normal income per household
Average disposable income per household
Average purchases per household
Average annual disposable income per person
Labor force participation rate
Unemployment rate*
Literacy rate for population over 15 years of age
Higher education rate for population over 15 years of age
Area (per person) of public parks, green open space,
children’s recreation, or athletic fields within the city
planning region
Number of firefighters per 100,000 persons
Number of fire trucks per 10,000 persons
Death rate of accident
Weight of garbage transported daily
Monthly dust fall
Atmospheric ozone concentration

Factor loading
0.907
0.647
0.961
0.986
0.995
0.984
0.980
0.936
0.807
0.807
0.896
0.896
–

0.756
0.955
0.598
0.732
0.642
0.583

% of variance
72.17

95.34

65.20
80.30
–

61.38

42.88

*

The numbers were negatively coded for the factor analysis

significant relationships between objective and
subjective measures were found in the other life
domains.
As the results indicate, it is not necessarily
appropriate to use disparity of objective conditions among cities and counties to depict quality
of life differences as felt by the inhabitants. The
negative correlation between objective condition
and subjective perception in education is an

example. As indicated in the results, residents
living in areas with better ▶ human capital, as
measured by ▶ literacy and ▶ higher education
rates, were less satisfied with the education
system. From the perspective of comparison theory, objective conditions affect subjective perceptions indirectly through comparisons with
other possible alternatives (Hagerty, 1999,
2000; Veenhoven, 1995). It is possible that

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4418

Objective and Subjective Indicators Correlations in Taiwan

Objective and Subjective Indicators Correlations in Taiwan, Table 3 Analysis of variance for subjective quality
of lifea
Mean
A. Taipei County
B. Yilan County
C. Taoyuan County
D. Hsinchu County
E. Miaoli County
F. Taichung County
G. Changhua County
H. Nantou County
I. Yunlin County
J. Chiayi County
K. Tainan County
L. Kaohsiung County
M. Pingtung County
N. Taitung County
O. Hualien County
P. Penghu County
Q. Keelung City
R. Hsinchu City
S. Taichung City
T. Chiayi City
U. Tainan City
V. Taipei City
W. Kaohsiung City
F-valueb
(Degrees of freedom)

Medical
service
2.5978
2.4809
2.7546
2.6429
2.6231n
2.7244
2.5971
2.6953
2.6508
2.7805
2.6349n
2.6800
2.6984
2.7016
2.6719
2.5109
2.6378
2.5859
2.6977
2.5702
2.6797
2.7128
2.6415
1.843*
(21, 3421)

Domestic
finances
2.5733
2.5942n
2.6667
2.6471
2.6357n
2.5926n
2.5714n
2.4855
2.6512
2.5522
2.5414
2.6519
2.4887
2.5338
2.6667
2.7244
2.6304n
2.5612
2.7111
2.5954n
2.6136n
2.5903n
2.6243n
1.247
(21, 3621)

Eta square

.012

.008

Work
2.7719
2.6630
2.8647
2.9010
2.9348
2.6147
2.6869
2.7113
2.7634n
2.8315
2.6961
2.8155
2.7561
2.7778n
2.8000
2.7439
2.8269
2.8119
2.7723n
2.6835
2.8438
2.7018
2.7094
1.854**
(21,
2523)
.016

Education
2.2890
2.4320
2.3125
2.2564
2.3893
2.5000
2.3615
2.3193
2.5210
2.5439
2.4386
2.4530
2.4505
2.6316
2.5000
2.4872
2.3033
2.1473
2.3846
2.4034
2.3250
2.1986
2.3311
4.266***
(21, 3225)

Leisure
2.6063
2.7537
2.7062
2.7023n
2.7612
2.6928n
2.6617
2.7154n
2.6504
2.7521
2.7480
2.7422
2.7364
2.8258
2.8031
2.6279
2.5682
2.7068n
2.6692
2.8130
2.7236
2.6098
2.6516
2.026**
(21, 3459)

Public safety
2.0747
2.1349
2.0398
2.0769
2.1024
2.1656
2.1716
2.0488
2.2500
2.3008
2.2080
2.0476
2.1930
2.3277
2.2479
2.3220
2.0076
2.0073
2.0923
2.2397
2.0909
2.1234
2.0065
3.290***
(21, 3383)

Environ.
quality
2.5714
2.8345
2.6538
2.8071
2.8849
2.7605
2.7114
2.8116
2.8029
2.7464
2.7899
2.7429
2.8561
2.8613
2.7698
2.8913
2.6739
2.6429
2.6815
2.7842
2.7194
2.7273
2.6879
3.347***
(21, 3709)

.028

.013

.021

.019

a

Due to missing values, the sample size for each city/county in each domain varied with 79 responses from Chiayi City in
work, which existed the most missing values than other domains
b
F-values were all significant at the 0.05 alpha rate, except for domestic finances
n
Denotes that the city/county did not differ from any of the other cities and counties in these quality of life domains
*
p<.05; **p<.01; ***p<.001

Objective and Subjective Indicators Correlations in Taiwan, Table 4 Nonparametric correlations of objective
and subjective quality of life (N ¼ 23)
Medical service Domestic finances Work Education Leisure Public safety Environ. quality
.072 .281
.518***
Kendall’s tau-b .146
.143
.091 .404**
**
Spearman’s rho .193
.163
.075 .597
.098 .412
.701***
**p < .01; *** p < .001

better-educated people have higher expectations,
based on comparison with others’ experiences or
their own in the past, for the functions that education systems may provide, resulting in lower
satisfaction than that of the less educated. On the

other hand, objective conditions on education
may not reach a universal criterion, as the needs
theory asserts, despite being superior to others.
This may, in turn, damage the subjective appreciation of individuals.

Objective and Subjective Indicators Correlations in Taiwan

Although comparison theory may explain the
negative relations between the rankings in education, the insignificant relations in other domains
need to be explained from an alternative perspective. It is possible that different theories were
applicable for either objective or subjective
dimensions but not both. It is clear that the request
for the improvement of the objective dimension
aims for the fulfillment of absolute criteria, as
needs theory has indicated (Veenhoven, 1995).
Subjective satisfaction, on the contrary, tends to
be the results of social comparison. As Veenhoven
(1996, p. 10) has indicated, “one’s general affect is
not very indicative for the adequacy of one’s
income.” The insignificant relations between subjective and objective rankings require an explanation from an alternative perspective. While no
previous study examined the consistency or disparity between objective and subjective indicators,
this study provides support to previous findings
that the level of subjective perceptions may not
correspond to the level of objective conditions (see
Lewis & Lyo, 1986; Schuessler & Fisher, 1985).
Since data used in this study only provided
descriptive results, more research with sophisticated analysis is needed to further demonstrate
the consistency or discrepancy in quality of life
measures and to provide a better view for theoretical discussions.
As it is difficult to obtain consensus on an
operational definition (Ziegler & Britton, 1981),
it remains a challenge to determine what should
be included among all of the available indicators.
While the results of this research are limited, it is
hoped that they still provide some contribution to
the issue and methodology of analyzing quality of
life in a contemporary country that has undergone
rapid development. It is possible that more
detailed study can explicate the complex relationship between objective and subjective measures
on a rural/urban continuum as it has evolved over
recent time.

Cross-References
▶ Comparison Theory
▶ Education

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▶ Environmental Quality
▶ Leisure
▶ Life Satisfaction
▶ Livability Theory
▶ Need Theory
▶ Quality of Life
▶ Rank-Order Correlation
▶ Social Indicators
▶ Subjective Indicators

References
Bradshaw, Y. W., & Fraser, E. (1989). City size, economic
development, and quality of life in China: New empirical evidence. American Sociological Review, 54,
986–1003.
Campbell, A., Converse, P. E., & Rodgers, W. L. (1976).
The perceived quality of life and its implications. In A.
Campbell, P. E. Converse, & W. L. Rodgers (Eds.),
The quality of American life (pp. 471–508). New York:
Russell Sage.
Diener, E., & Lucas, R. E. (2000). Explaining differences
in societal levels of happiness: Relative standards,
need fulfillment, culture, and evaluation theory. Journal of Happiness Studies, 1, 41–78.
Diener, E., Oishi, S., & Lucas, R. (2003). Personality,
culture, and subjective well-being: Emotional and cognitive evaluations of life. Annual Review of Psychology, 54, 403–425.
Executive Yuan, R. O. C. (Taiwan), (2000). Statistical
indicators of Hsien and cities. Available online
at http://win.dgbas.gov.tw/dgbas03/bs8/city/default.
htm. Accessed January 19, 2007.
Hagerty, M. R. (1999). Unifying livability and
comparison theory: Cross-national time-series analysis of life-satisfaction. Social Indicators Research, 47,
343–356.
Hagerty, M. R. (2000). Social comparisons of income in
one’s community: Evidence from national surveys of
incomes and happiness. Journal of Personality and
Social Psychology, 78, 764–771.
Kao, C. H. C., & Liu, B. C. (1984). Socioeconomic
advance in the Republic of China (Taiwan): An
intertemporal analysis of its quality of life indicators.
American Journal of Economics and Sociology, 43(4),
399–412.
Lewis, S., & Lyo, L. (1986). The quality of community
and the quality of life. Sociological Spectrum, 6,
397–410.
Schuessler, K. F., & Fisher, G. A. (1985). Quality of life
research and sociology. Annual Review of Sociology,
11, 129–149.
Schyns, P. (1998). Cross-national differences in happiness: Economic and cultural factors explored. Social
Indicators Research, 43, 3–26.

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Veenhoven, R. (1995). The cross-national pattern of happiness: Test of predictions implied in three theories of
happiness. Social Indicators Research, 34, 33–68.
Veenhoven, R. (1996). Developments in satisfaction
research. Social Indicators Research, 37, 1–45.
Wu, P., & Shih, P., (1996) The comparison of quality of
life among counties and cities in Taiwan. Taiwan
Economy Monthly, 240, 26–38.
Ziegler, J. A., & Britton, C. R. (1981). A comparative
analysis of socioeconomic variations in measuring the
quality of life. Social Science Quarterly, 62, 303–312.

Objective and Subjective Life Quality
▶ Objective and Subjective Indicators
Correlations in Taiwan

Objective and Subjective Nearness
to Death
Dana Kotter-Gr€
uhn
Department of Psychology and Neuroscience,
Duke University, Durham, NC, USA

Synonyms
Age distance to death; Ageism; Closeness to
death; Mortality salience; Perceived distance
to death; Perception of remaining lifetime;
Socioemotional selectivity theory; Terror
management theory; Time distance to death

Definition
Subjective nearness to death refers to individuals’
perception of their remaining lifetime, that is,
how close they are or feel to death. Objective
nearness to death refers to the actual time span
a person is close to death.

Description
How long am I going to live? How old will I be
when I die? Nobody can answer these questions

Objective and Subjective Life Quality

with absolute certainty, but can we at least try? Do
humans have the ability to correctly judge how
long they are going to live and how close they are
to dying? Such questions are addressed by the
concepts of objective and subjective nearness to
death. Whereas objective nearness to death refers
to the number of months or years a person actually
lives until he/she dies, subjective nearness to death
refers to the person’s individual perception of how
close he/she is to dying. Both objective and subjective nearness to death are particularly relevant
in older (and terminally ill) persons because their
lifetime is in fact coming to an end. Therefore, the
following overview of research pertaining to
objective and subjective nearness to death focuses
primarily on older adults.
Objectiveness Nearness to Death
Even though objective closeness to death can be
estimated in some cases, an absolutely accurate
value for this variable can only be determined
retroactively (i.e., after a person’s death). The
estimation of objective closeness to death is
a relevant undertaking, for instance, in medicine
and economy. Physicians have to estimate the
remaining lifetime of a terminally ill patient,
and insurance companies use estimates of ▶ life
expectancy and remaining lifetime to determine
a person’s eligibility and/or premiums for
purchasing life insurance. Similarly, retirement
contributions are strongly influenced by objective
estimates of one’s remaining life- and work time.
Objective nearness to death has further gained
some popularity in psychological science in
which researchers have begun to examine how
psychological or physical functioning changes as
a function of distance (or closeness) to death.
Using longitudinal data of deceased persons for
whom we have information about the time point
of death allows investigating how psychological
functioning changes as individuals approach
death. This research has shown that constructs
such as cognitive functioning, well-being, or
positive self-perceptions of aging not only
decrease as a function of age but also decline as
individuals approach death (e.g., B€ackman &
MacDonald, 2006; Gerstorf, Ram, Röcke,
Lindenberger, & Smith, 2008b; Kotter-Gr€uhn,

Objective and Subjective Nearness to Death

Kleinspehn-Ammerlahn, Gerstorf, & Smith,
2009). In fact, in many cases, statistical models
that contrast age-, time-, and distance-to-deathrelated changes in psychological functioning
demonstrate that distance to death seems to be
a time metric that better captures changes in old
age than the time metrics of age or time.
Pushing the notion of distance-to-death-related
change a little bit further, some longitudinal studies are now examining whether the phase shortly
before death (as opposed to the time farther away
from death) is characterized by an accelerated
decline in psychological functioning. This idea
of “terminal decline” was already introduced
into the literature several decades ago, for
instance, by Kleemeier (1962) as well as Riegel
and Riegel (1972), but the development of statistical tools such as dual-change-point models now
allow us to empirically test whether or not terminal decline in fact takes place. For instance, using
longitudinal data from the German Socioeconomic Panel Study, Gerstorf et al. (2008a) demonstrated that ▶ life satisfaction in older adults
declined over the 22-year study period, and this
decline could be differentiated into two phases,
a preterminal decline phase and a terminal decline
phase. In comparison to the preterminal decline
phase, the decline in life satisfaction was about
three times as steep in the very last years before
participants’ death, the so-called terminal decline
stage. Similar findings have been reported for
cognitive functioning (e.g., Sliwinski et al. 2006).
Objective closeness to death also plays a role
in studies aiming at predicting ▶ mortality status
(i.e., death versus alive) using psychological
variables. Such studies typically contrast the psychological functioning at a specific time point of
participants who either survived or died during
the subsequent study period. If it is found that
a variable predicts mortality, this information is
further used to calculate median survival times
for persons who scored either high or low on the
respective variable. For example, in a study by
Kotter-Gr€
uhn et al. (2009), aging satisfaction
assessed at the first measurement occasion of
a longitudinal study predicted mortality risk
16 years later. That is, even after controlling for
variables that are typically related to a higher

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likelihood of dying (e.g., higher age, bad health),
higher aging satisfaction at time point 1 was
related to a lower risk of dying during the study
period. Furthermore, it was demonstrated that
persons who were satisfied with their age and
aging at time point 1 had a median survival
time of 6.2 years, whereas persons who were
less satisfied with their aging only survived an
average of 4.0 years. Thus, from knowing a
person’s level on a specific variable, the objective
nearness to death could be calculated. Mortality
analyses have become very popular over the
past years, and there is now ample evidence
that a variety of psychological variables
(e.g., cognitive functioning, well-being, subjective health) predict mortality risk and allow
calculating estimates of older adults’ closeness to
death (e.g., Idler & Benyamini, 1997; Mroczek &
Spiro, 2007). It needs to be taken into consideration, however, that such calculations are
mainly performed retroactively, that is, they
rely on data of deceased participants. It is questionable whether we could and should apply the
calculated median survival times to living older
adults, for instance, by measuring their expression on a specific variable and then telling
them that in previous studies people who had
comparable scores lived on average another
x years/months.
Subjective Nearness to Death
Whereas it is impossible to assess a person’s
objective closeness to death during his or her
lifetime, we can examine how close people feel
to dying, that is, their subjective perception of
being close to death. Similar to objective nearness to death, subjective nearness to death has
been a topic of interest, especially in the fields of
economy and psychology. For instance, to understand risk perception, financial planning, and
▶ decision making when it comes to purchasing
life insurance and setting contributions to retirement plans, economists have examined the concept of subjective longevity expectations (i.e., the
subjective probability to survive to a certain age).
Psychologists are mainly interested in the psychological correlates and consequences of feeling
close to death. Several psychological theories

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underscore the importance of being aware of
one’s closeness to death. For instance, terror
management theory posits and empirically demonstrates that being reminded of one’s own mortality (so-called mortality salience) can influence
peoples’ perception, ▶ attitudes, ▶ values, and
behaviors (Burke, Martens, & Faucher, 2010).
Whereas in everyday life young people are
unlikely to be reminded of their own mortality
on a regular basis, older adults may frequently be
reminded of being relatively close to death.
Those reminders may come in the form of their
own age, changes in their health status and memory performance, or the death of friends and
relatives. In fact, younger adults consider older
adults to be “living symbols of time running out”
(Martens, Greenberg, Schimel, & Landau, 2004,
p. 1534). Thus, older adults may be more prone to
mortality salience and its consequences. In the
context of lifespan developmental psychology,
subjective nearness to death can also be considered under the umbrella terms of future orientation, future perception, or future time
perspective. The latter plays a major role in
socioemotional selectivity theory (Carstensen,
2006) which posits that perceiving one’s future
or lifetime as limited is predictive of shifting
one’s focus from knowledge-related goals to
emotional goals and emotionally rewarding
▶ social interactions (Lang, 2000). Similarly,
self-regulatory theories propose that the perception of one’s lifetime running out is linked to
a shift from extrinsic-instrumental goals to intrinsic and ego-transcending goals and values
(Brandtst€adter, Rothermund, Kranz, & K€uhn,
2010). That is, the goals people set for themselves
typically are adapted to one’s estimated
remaining lifetime. A person who perceives him
or herself to be close to dying is unlikely to set
a goal in the far future or a goal that can only be
achieved if actively pursued over many years.
This is supported by research showing that older
adults set fewer goals and prioritize goals differently than younger adults do (Ebner, Freund, &
Baltes, 2006; Prenda & Lachman, 2001).
Considering the consequences that the subjective perception of being close to death can have,

Objective and Subjective Nearness to Death

the question arises as to whether the subjective
estimation of time running out is accurate.
A study by Kotter-Gr€uhn and Smith (2010) demonstrated that in old age a relationship between
objective and subjective nearness exists. First,
over a 16-year time period, subjective nearness
to death was a significant predictor of objective
nearness to death. Those participants who indicated feeling close to death at the first measurement occasion were more likely to have died over
the 16-year study period (3.8 % of persons in this
group were still alive after 16 years) than those
who reported not feeling close to death (16 % of
persons in this group were still alive after
16 years). More precisely, the people who didn’t
feel close to death survived an average of
6.1 years, whereas those who felt close to death
had a median survival time of only 3.6 years.
Second, exploring the dynamic relationship
between objective and subjective nearness to
death of participants who had died during the
study period, it was found that when participants’
objective nearness to death increased, subjective
nearness to death increased as well. That is, over
a long time span of old age, individuals accurately adjusted their subjective nearness to death
as their remaining lifetime declined. This finding
remained the same even after controlling for variables that typically predict mortality (e.g., subjective and objective health).
Note that subjective nearness to death is
different from a person’s desired lifetime,
that is, the age to which people want to live
(e.g., Lang, Baltes, & Wagner, 2007). The subjective estimation of how close one is to dying
may be in part influenced by or related to one’s
desire to live to a certain age. However, desired
lifetime and subjective closeness to death can
also be independent from each other. For
instance, a person may feel close to death but at
the same time he/she wants to live another
20 years; or a person may not feel close to death
at all but he/she actually has no desire to live
several more years. Subjective nearness to death
is further related to but still different from
the subjective probability of surviving to a
specific age.

Objective and Subjective Nearness to Death

How do older adults know they are
approaching death? Of course, especially in old
age, there is a logical relationship between age or
time going by and closeness to death. In that
regard, however, we would expect that all older
adults always feel close to death, but this is not
supported by empirical data. There are
interindividual differences in how close older
adults feel to dying, and those differences correspond to their objective closeness to death. So
far, it can only be speculated how people actually evaluate/estimate how close they are to
dying. When evaluating their nearness to death,
older adults most likely take into account their
own age and health status, they may compare
their own life circumstances and health status to
that of friends or relatives who recently died,
they may evaluate whether or not they show
symptoms that friends or relatives showed
shortly before they died. As it is widely known
that longevity runs in families, persons might
use the age at which close relatives died as
a reference point for calculating their own
remaining lifetime. Furthermore, a person’s
evaluation of being close to death may be related
to his/her will to live, the desire to live to
a certain age, or the valuation of life under
given circumstances. The relationship between
objective and subjective nearness to death might
further be a reflection of self-fulfilling prophecies in that those who believe that their life is
coming to an end in fact die earlier because of
this belief. On the other hand, some people
might never report feeling close to dying either
because they deny that life is coming to an end,
they are scared of dying, or because they are
overly optimistic with regard to their estimated
remaining lifetime.
In conclusion, both objective and subjective
nearness to death play important roles in understanding and adequately describing developmental trajectories in very old adults. Even
though they are different from each other, objective and subjective nearness to death are related
to each other, and subjective closeness to death
can be used as a predictor of objective nearness
to death.

4423

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References
B€ackman, L., & MacDonald, S. W. S. (2006). Death and
cognition. European Psychologist, 11, 224–235.
Brandtst€adter, J., Rothermund, K., Kranz, D., & K€
uhn, W.
(2010). Final decentrations: Personal goals, rationality
perspectives, and the awareness of life’s finitude.
European Psychologist, 15, 152–163.
Burke, B. L., Martens, A., & Faucher, E. H. (2010). Two
decades of terror management theory: A meta-analysis
of mortality salience research. Personality and Social
Psychology Review, 14, 155–195.
Carstensen, L. L. (2006). The influence of a sense of time
on human development. Science, 312, 1913–1915.
Ebner, N. C., Freund, A. M., & Baltes, P. B. (2006).
Developmental changes in personal goal orientation
from young to late adulthood: From striving for gains
to maintenance and prevention of losses. Psychology
and Aging, 21, 664–678.
Gerstorf, D., Ram, N., Estabrook, R., Schupp, J., Wagner,
G. G., & Lindenberger, U. (2008a). Life satisfaction
shows terminal decline in old age: Longitudinal evidence from the German Socio-Economic Panel Study
(SOEP). Developmental Psychology, 44, 1148–1159.
Gerstorf, D., Ram, N., Röcke, C., Lindenberger, U., &
Smith, J. (2008b). Decline in life satisfaction
in old age: Longitudinal evidence for links
to distance-to-death. Psychology and Aging,
23, 154–168.
Idler, E. L., & Benyamini, Y. (1997). Self-rated health and
mortality: A review of twenty-seven community studies. Journal of Health and Social Behavior, 38, 21–37.
Kleemeier, R. (1962). Intellectual changes in the senium.
Proceedings of the American Statistical Association,
1, 290–295.
Kotter-Gr€
uhn, D., Gr€
uhn, D., & Smith, J. (2010).
Predicting one’s own death: The relationship between
subjective and objective nearness to death in very old
age. European Journal of Ageing, 7, 293–300.
Kotter-Gr€
uhn, D., Kleinspehn-Ammerlahn, A., Gerstorf,
D., & Smith, J. (2009). Self-perceptions of aging
predict mortality and change with approaching death:
16-year longitudinal results from the Berlin aging
study. Psychology and Aging, 24, 654–667.
Lang, F. R. (2000). Endings and continuity of social
relationships: Maximizing intrinsic benefits within
personal networks when feeling near to death. Journal
of Social and Personal Relationships, 17, 155–182.
Lang, F. R., Baltes, P. B., & Wagner, G. G. (2007).
Desired lifetime and end-of-life desires across adulthood from 20 to 90: A dual-source information model.
Journals of Gerontology: Psychological Sciences, 62,
268–276.
Martens, A., Greenberg, J., Schimel, J., & Landau, M.
(2004). Ageism and death: Effects of mortality
salience and perceived similarity to elders on reactions
to elderly people. Personality and Social Psychology
Bulletin, 30, 1524–1536.

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4424

Mroczek, D. K., & Spiro, A., III. (2007). Personality
change influences mortality in older men. Psychological Science, 18, 371–376.
Prenda, K. M., & Lachman, M. E. (2001). Planning for the
future: A life management strategy for increasing
control and life satisfaction in adulthood. Psychology
and Aging, 16, 206–216.
Riegel, K. F., & Riegel, R. M. (1972). Development, drop,
and death. Developmental Psychology, 6, 306–319.
Sliwinski, M. J., Stawski, R. S., Hall, C. B., Katz, M.,
Verghese, J., & Lipton, R. (2006). Distinguishing
preterminal and terminal cognitive decline. European
Psychologist, 11, 172–181.

Objective and Subjective Poverty
Eszter Siposne Nandori
Institute of World and Regional Economics,
University of Miskolc, Miskolc, Hungary

Synonyms
Deprivation from happiness and deprivation from
wealth; Informal and official poverty; Material
and subjective poverty; Qualitative and quantitative approaches of poverty

Definition
▶ Subjective poverty is the subjective perception
of ▶ deprivation. Two approaches can be distinguished in defining subjective poverty. On the
one hand, poverty can be defined by examining
who is in general considered to be poor. On the
other, it can also be defined by collecting their
beliefs about their own position in a system of
inequalities.
In most of the cases, however, poverty is
defined based on some objective measure.
Income level is the most widely used measure to
do so. Besides, number of children, age, ▶ education attainment, labor market position, or level
of comfort can also be applied as a basis for
defining objective poverty. Three main concepts
of objective poverty can be distinguished: absolute, relative, and (welfare) political.

Objective and Subjective Poverty

Description
Two main concepts of poverty are distinguished
in the poverty literature: subjective and objective
poverty (refer to Table 1).
Subjective Poverty Concepts
Subjective concept of poverty was elaborated by
two research groups. Van Praag (1971) worked
out the Income Evaluation Question (IEQ) to
collect data on subjective well-being. Deleeck
and his staff defined CSP (subjective poverty
line). Subjective poverty concept is often used
in two ways. On the one hand, poverty can be
defined by examining who people consider to be
poor. It can also be defined by collecting peoples’
beliefs about their own position in a system of
social inequalities (Spéder, 2002). Subjective
poverty concept can reflect ▶ subjective wellbeing.
▶ Happiness, a part of subjective well-being,
is considered to be the ultimate goal of human life
by a reasonable part of the society. Happiness
research is relevant for economists for multiple
reasons. It can inform economic policy decisions
to make it possible to reach a Pareto-improving
proposal (actions that do not entail costs for other
individuals). Further, it highlights the importance
of institutional conditions such as the quality of
governance, the size of social capital, or the rule
of law on subjective well-being, and it can also
help in understanding the formation of subjective
well-being (Frey & Stutzer, 2002) and peoples’
values, behavior, and belief (Samman, 2007).
People can be asked about the amount of
money they consider to be necessary in order to
satisfy the ▶ minimal needs related to a minimal
human lifestyle. Subjective poverty threshold can
be defined based on the weighted average of per
capita or per household amount of what people
believe to be necessary. Information on subjective poverty and subjective well-being is usually
derived from household surveys. The World
Value Survey, for example, measures subjective
well-being with the following questions: “Taking
all things together, would you say you are very
happy, quite happy, not very happy, or not at all
happy?” or “All things considered, how satisfied

Objective and Subjective Poverty

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Objective and Subjective Poverty, Table 1 Concepts of poverty
Concepts of poverty
Objective Absolute

Income
Subsistence level
Regional minimum
Relative
Living below the 50 or 60 % of mean or median income
Lower decile, quintile
(Welfare) political Existence minimum
Minimum pension
Subjective
Subjective poverty

Living conditions
Not processing certain items
Being in crisis
Deprivation index

Minimal living conditions

Source: own compilation based on Spéder (2002, p. 51)

are you with the your life as a whole these days?.”
Subjective well-being and poverty can also be
measured by other subjective measures, like personality or the evaluation of friends and relatives.
Stevenson and Wolfers (2008) conclude that selfreporting standard of living and other subjective
measures of well-being are usually highly
correlated.
The intertemporal assessment of subjective
poverty can be a big issue because of the excess
amount of available data. The frequent changes in
the data collection methods (like scale differences in the measurement over time) make it
very difficult to draw reliable conclusions
(Stevenson & Wolfers, 2008).
Objective Poverty Concepts
Besides subjective concept of poverty, three main
poverty conceptions are distinguished in the literature (refer to Table 1), which are called objective poverty concepts.
Objective or official concepts of poverty are
usually derived from ideology. Ideologies – no
matter if they are ethical, legal, political, religious, etc. – are realized in institutions. Ideologies always belong to classes; therefore, they can
never be socially neutral. The ruling ideology
usually forms the essential power of the dominant
class. This ideology is elaborated by the state
apparatuses (Poulantzas 1978). Althusser
(2008) says that ideology always interprets individuals as subjects. The term subject refers to
(1) a free subjectivity, a center of initiatives, author
of and responsible for its actions; (2) a subjected
being, who submits to a higher authority, and is
therefore stripped of all freedom except that of freely
accepting his submission. (Althusser, 2008, p. 56)

That is why all subjects “work by themselves.”
In terms of poverty and inequalities, it means that
a political concept of poverty reflecting the ideology of the ruling class blames only individuals
for being poor, and not politics or economy. This
▶ welfare political concept derived from ideologies is independent of the beliefs of the dominated classes, who are more endangered by
poverty and social exclusion than the ruling
class. Thus, in order to get a clear understanding
of poverty, the examination of the differences
between political concept of poverty and subjective perceptions is of crucial importance.
Welfare political concept of poverty is important because many kinds of public aid (social
supports, family allowances, grants for the underprivileged) are distributed using it. In most of the
cases, some income level is used to define welfare
political poverty. A threshold for this can be
a certain portion of the minimal pension (Spéder,
2002). Besides, old age, the number of children, or
the education level can also be applied as poverty
lines. Any deviation between these thresholds and
the subjective poverty lines has serious consequences and distorts the welfare system.
Absolute concepts of poverty assume that
minimum material needs can be defined regardless of space and time. Those who are not able to
satisfy these needs are considered to be poor. The
subsistence level is the amount of money that
ensures the fulfillment of moderate needs related
to a minimal lifestyle. When subsistence levels
are different for the different regions of a country,
they are called regional minimum. The World
Bank applies absolute poverty thresholds
when it defines poverty as living below USD

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4426

1.25 or 2 a day. The use of these thresholds makes
international comparison of poverty possible
across countries. It, however, ignores wage and
price differences that can play an important role
in defining the sum of money necessary to make
ends meet.
The relative conceptions define poverty as
being below some relative poverty threshold.
People can be considered to be poor if they fall
below some average wealth level of the society to
a certain extent (e.g., 50 or 60 % of mean or
median income). The other approach using the
relative poverty concept defines poverty line as
an income level below which a certain part (one
tenth or one fifth) of the population lives
(Hegedus & Monostori, 2005). The European
Union worked out the system of Laeken indicators in 2001, which defines several – mainly
relative – measures of poverty. Its application
makes it possible to compare the poverty of different level NUTS regions. The primary Laeken
indicators are the following:
• Indicator 1a: At-risk-of-poverty rate, by age
and gender
• Indicator 1b: At-risk-of-poverty rate, by most
frequent activity status and gender
• Indicator 1c: At-risk-of-poverty rate, by
household type
• Indicator 1d: At-risk-of-poverty rate, by
accommodation tenure status
• Indicator 1e: At-risk-of-poverty threshold
(illustrative values)
• Indicator 2: Inequality of ▶ income distribution – S80/S20 income quintile share ratio
• Indicator 3: At-persistent-risk-of-poverty rate,
by gender (60 % national median)
• Indicator 4: Relative median at-risk-ofpoverty gap, by gender
• Indicator 5: Regional cohesion (dispersion of
regional employment rates)
• Indicator 6: Long-term unemployment rate, by
gender
• Indicator 7: Persons living in jobless households, by age and gender
• Indicator 8: Early school leavers not in education or training, by gender
• Indicator 9: Life expectancy at birth, by
gender

Objective and Subjective Poverty

• Indicator 10: Self-defined health status by
income quintile by gender (Guio, 2004)
Relative income concerns may help understanding many economic phenomena (Carlsson,
Johansson-Stenman, & Martinsson, 2007), like
aggregate consumption and saving patterns
(Basmann, Molina, & Slottje, 1988), wage formation (Agell & Lundborg, 2003), labor supply
(Neumark & Postlewait, 1998), or the
overconsumption of goods consumed primarily
to demonstrate wealth and success (Carlsson
et al., 2007).
Relationship Between Subjective and
Objective Concepts of Poverty
Neither absolute and relative nor political poverty measures introduce any explicit role for
nonmonetary components, and therefore, they
are not based on happiness. Thus, they presumably significantly differ from subjective poverty
assessment. Many studies have focused on the
correlation between subjective indicators and
material well-being. There are two main
approaches in these studies: the questionnaireexperimental approach and the subjective
approach (Castilla, 2009). Results from the questionnaire-experimental approach suggest that relative income position is as important as the
absolute one (Carlsson et al., 2007; JohanssonStenman & Martinsson, 2006), while studies
using subjective approach conclude that relative
poverty is usually more important (Clark,
Frijters, & Shields, 2008; Easterlin, 1995).
Behavioral economics takes the influence of
social context into account in people’s assessment about their well-being. It implies that people prefer not only to have a high income level but
also to have more than others (Carlsson et al.,
2007). If relative income position has any effect
on subjective poverty assessments, the improvement of subjective well-being requires reducing
inequality in the society. If, however, the poor are
too concerned with their everyday survival to
take their relative position into consideration,
policies focusing on the alleviation of absolute
poverty can be justified. It is crucially important
to reveal the differences between subjective and
objective poverty assessments since even the

Objective and Subjective Poverty

best-designed policy intervention can fail
because of ignoring the subjective assessment of
well-being in poverty assessment (Fafchamps &
Shilpi, 2008).
The so-called ▶ Easterlin paradox states that
wealthier people tend to be happier than the
poorer ones, but above a certain level of per capita
average income (somewhere between US$10,000
and 20,000), there is no correlation between average income and subjective well-being (Easterlin,
1995). At the microlevel, however, positive correlations have been found between individual
income and individual assessment of subjective
well-being (Clark et al., 2008). Layard (2002) also
stated that “once a country has over $15,000 per
head, its level of happiness appears to be independent of its income per head” (Layard, 2002, p. 17).
Ravallion and Lokshin (2002) also explored the
lack of this relationship for Russia. Most of the
Russian adults who feel that they are poor are not
classified as such in official statistics, and most of
the people who are classified to be poor do not feel
that they are. Deaton (2010) also concluded that
subjective assessment of well-being and income
level does not correlate.
Easterlin (1974) analyzed the relationship
between material and subjective well-being
across countries and also within countries over
time. He concluded that in case of wealthy countries, material well-being has little or no impact
on subjective assessment of well-being in both
cases. Other findings, however, indicated that
wealthier people tend to be happier when the
analysis is carried out within countries. Further
studies revealed that these seemingly
disconcordant findings can be explained by relative income comparisons (Stevenson & Wolfers,
2008).
Some studies about developed countries state
that subjective well-being of individuals depends
not only on their own standard of living but also
on their relative income and relative
deprivation. It means that higher earnings of the
others are associated with lower level of wellbeing, controlling for the individual’s own
income (Easterlin, 1995; Frey & Stutzer, 2002;
Layard, 2002; Luttmer, 2005). A study on Nepal
and Malawi, however, proved that relative

4427

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position of the individuals has an effect on the
subjective perception of poverty only among
upper income households. The poor care only
about absolute deprivation (Fafchamps & Shilpi,
2008).
Stevenson and Wolfers (2008) highlight that
the existence of a satiation point is quite uncertain. They argue that the curvilinear nature of the
relationship between the absolute income level
and the subjective assessment of well-being can
be eliminated by taking into account the logarithm of the average income level instead of its
absolute value. When subjective well-being is
examined in the function of the log income
level, the nature of the relationship seems to be
linear. They also highlight that there is less consensus about the degree to which objective and
subjective well-being and poverty are correlated.
The fact that objective and subjective wellbeing are independent of each other implies that
it is no more necessary to place the increase of
material well-being (p.e. the promotion of GDP
growth) into the focus of economic policy. In this
case, government policy should focus on the
maximization
of
subjective
well-being
(Stevenson & Wolfers, 2008).

O
Cross-References
▶ Beliefs About Poverty
▶ Poverty Lines
▶ Poverty Measurement

References
Agell, J., & Lundborg, P. (2003). Survey evidence on
wage rigidity and unemployment: Sweden in the
1990s. The Scandinavian Journal of Economics, 105,
15–30.
Althusser, L. (2008). On ideology. London, New York,
Verso.
Basmann, R. L., Molina, D. J., & Slottje, D. J. (1988).
A note on measuring Veblen’s theory of conspicuous
consumption. The Review of Economics and Statistics,
70, 531–535.
Carlsson, F., Johansson-Stenman, O., & Martinsson, P.
(2007). Do you enjoy having more than others? Survey
evidence of positional goods. Economica, 74,
586–598.

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4428

Castilla, C. (2009). Objective versus subjective
poverty: Are income positional concerns influencing
subjective poverty assessments? In Midwest
international economic development conference,
Minneapolis.
Clark, A. E., Frijters, P., & Shields, M. (2008). Relative
income, happiness and utility: An explanation for
the Easterlin paradox and other puzzles. Journal of
Economic Literature, 46, 95–144.
Deaton, A. (2010). Price indexes, inequality, and the measurement of world poverty. American Economic
Review, 100(1), 5–34.
Easterlin, R. A. (1974). Does economic growth improve
the human lot? Some empirical evidence. Nations and
households in economic growth, 89, 89–125.
Easterlin, R. A. (1995). Will raising the incomes of all
increase the happiness of all? Journal of Economic
Behavior and Organisation, 27, 35–47.
Fafchamps, M., & Shilpi, F. (2008). Subjective welfare,
isolation, and relative consumption. Journal of Development Economics, 86, 43–60.
Frey, B. S., & Stutzer, A. (2002). What can economists
learn from happiness research? Journal of Economic
Literature, 40, 402–435.
Guio, A. -C. (2004). The Laeken indicators: Some results
and methodological issues in acceding and candidate
countries. In EUROSTAT – Statistical office of the
European communities, Vilnius, Lithuania, p. 2.
Hegedus, P., & Monostori, J. (2005). A szegénység és a tá
rsadalmi kirekesztodés méroszámai 2005. Elméleti
megalapozás [Measures of poverty and social exclusion 2005. Theoretical basics]. Budapest, Hungary:
KSH Népességtudományi Kutató Intézet.
Johansson-Stenman, O., & Martinsson, P. (2006).
Honestly, why are you driving a BMW? Journal
of Economic Behavior and Organization, 60, 129–146.
Layard, R. (2002). Happiness: Has social science a clue
(Lionel Robbins Memorial lectures 2002/3). London
School of Economics. Retrieved March 3–5, 2003
from
http://cep.lse.ac.uk/events/lectures/layard/
RL030303.pdf
Luttmer, E. F. P. (2005). Neighbors as negatives: Relative
earnings and well-being. Quarterly Journal of
Economics, 120, 963–1002.
Neumark, D., & Postlewait, A. (1998). Relative income
concerns and the rise in married women’s employment. Journal of Public Economics, 70, 157–183.
Poulantzas, N. (1978). State, power, socialism. London.
Ravallion, M., & Lokshin, M. (2002). Self-rated economic
welfare in Russia. European Economic Review, 46,
1453–1473.
Samman, E. (2007). Psychological and subjective
well-being: A proposal for internationally comparable
indicators. Oxford Development Studies, 35, 459–486.
Spéder, Z. (2002). A szegénység változó arcai: Tények
és értelmezések [Changing forms of poverty:
Facts and interpretations]. Budapest, Hungary:
Andorka Rudolf Társadalomtudományi Társaság
Századvég.

Objective Conditions, Subjective Assessments of
Stevenson, B., & Wolfers, J. (2008). Economic growth
and subjective well-being: Reassessing the Easterlin
paradox. Brooking Papers on Economic Activity, 39,
1–102.
Van Praag, B. M. S. (1971). The welfare function of
income in Belgium: An empirical investigation. European Economic Review, 2, 337–369.

Objective Conditions, Subjective
Assessments of
▶ Systemic Quality of Life Model (SQOL)

Objective Index of Quality of Life
Developed for the Municipalities of
the Barcelona Province
Vicente Royuela
AQR-IREA Research Group, University of
Barcelona, Barcelona, Spain

Definition
The Composite Quality of Life Index (CQLI) for
the province of Barcelona is a weighted (a priori)
arithmetic average index of partial indicators
that express the relative standardized position
of every individual (municipality, subsystem, or
system) after combining the variability of all variables, with a Paasche-type temporal aggregation. To sum up, the CQLI is an aggregate index
that is computed using partial information of
every dimension of quality of life. It is computed
at the municipal level, for the 1991–2004 period.

Description
Objective index of quality of life developed for
municipalities of the Barcelona Province.
Quality of life has been investigated and
measured for many years in order to carry out
comparisons between cities and urban areas

Objective Index of Quality of Life Developed for the Municipalities of the Barcelona Province

(Louis, 1976; Liu, 1978; Blomquist, Berger &
Hoehn, 1988; Rogerson, Findlay, Morris &
Coombes, 1989; Stover & Leven 1992), states
(Liu, 1973), or nations (Slottje, 1989; Hirschberg,
Maasoumi & Slottje 1991; Dasgupta & Weale,
1992, among others). Several methodologies
have been used and have been adapted to specific
contexts.
In Royuela et al. (2003) a composite index for
municipalities at the Barcelona Province (Spain)
was developed. That index was developed to
allow for simultaneous comparison of time
periods and areas, the aggregation of variables
with different units of measurement, the aggregation of several constituents of well-being, and
solving specific problems involving small areas.
The analysis was focused on the province of
Barcelona, one of the four provinces in the region
of Catalonia. Catalonia (NUTS II in the European
administrative classification) is one of Spain’s
most developed regions, located in the northeast
of the country. The region is divided in
four administrative provinces (NUTS III in the
European administrative classification), and
Barcelona is the most populated, with 76 % of
the region’s inhabitants: 4,628,277 in 1996.
Together with Madrid it is Spain’s most
populated and urbanized province. It has 314
municipalities, organized in 11 administrative
groups, named comarques. Royuela and Suriñach
(2005) worked with four territorial dimensions:
comarques (of which there are 11), urban
systems (24), urban subsystems (48), and
municipalities (314).
The methodology on which the study was
based was developed for the USA in Liu (1978).
The main idea is to build a composite index using
intermediate indexes computed using basic data
or other indexes. The final Composite Quality of
Life Index, CQLI, considered three main
components:
1. Individual Opportunities for Progress: this
first element measures personal features of
people living in the municipality. It takes
into account wealth, labor, education level,
health level, and mobility possibilities.
2. Index of Social Equilibrium: this index considers the social inequalities in the area (sexual

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inequality, migration, housing access, commuting, and services for old people).
3. Community Conditions of Life: this covers
global services such as housing, public transport, education and health services, environment, culture, and local taxes.
The period of analysis in that paper finally
covered 4 years: 1991, 1996, 1997, and 1998.
The applied statistical method had to meet five
requisites:
1. The index has to be able to aggregate quality
of life indicators calculated with different
units of measurement.
2. The aggregation process has to be able to
compare quality of life indicators with a high
level of different relative dispersion.
3. The index has to allow the construction of
a scale that lets the data talk, that is, that
reflects the statistical characteristics of the
data.
4. The final index has to allow for a comparison
over time: when a system’s basic variables
rise, the final index has to increase.
5. If the relative size of the systems changes over
time, the index has to condense this information without overvaluing (undervaluing) the
result for a specific system.
These criteria are the basis for our index, I, as
a linear function of several, K, quality of life
attributes (X). Each attribute measures quality
of life, indicating the extent to which
a municipality is above (below) the provincial
average, which is equal to 100 in the base year.
The structure of the CQLI reduces the dimensions
at each level. Every attribute, Xf, is originally
measured in its own units but needs to be
redefined in terms of quality of life. We do so
taking a relative measure, which converts the
result into a percentage. If municipality i has
a value in the f attribute equal to Xif, then we
say that we can measure how far municipality i
differs from the provincial average in terms of
quality of life merely by computing the ratio over
the variable’s mean.
Then, the final index, I00 i, is a
 linear function

of the attributes’ vector Y i ; Y i ¼ Y1 i ; . . . ; YK i :
I 00i ¼ Y i W;

(1)

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Objective Index of Quality of Life Developed for the Municipalities of the Barcelona Province

where W ¼ ðw1 ; . . . ; wK Þ are the weights given to
every attribute.
Differences between municipalities can be
expressed in a dispersion measurement, for
example, the variance

where R is the correlation matrix between the
standardized attributes. This is the reason for
computing the final standardized positions (number of standard deviations away from the trend) of
the municipalities as:

VARðI 00 Þ from i ¼ 1 to N;

I ¼ ZW=ðW 0 RW Þ:

where N is the total number of municipalities.
We understand that this variance is useful
information about attribute Yf. If we only had
one quality of life attribute, then the measurement
of quality of life would be defined by this particular variance. But as there is more than one attribute in each index, a general measurement for
each aggregate index needs to be defined. Following (1), the total amount of information provided by the final index is:

As we built the final index measurement in (2),
now we only have to add it to the standardized
positions of all municipalities defined in (3). In
order to make it more comprehensible, we have
included a level to the final measurement (100 in
the base year). So, the CQLI is:

VARðI 00 Þ ¼ VARðYW Þ ¼ W 0 VARðYÞW:

(2)

So, the index information is equal to the
weighted variance and covariance matrix of
the attributes. This is the measurement of
quality of life that we will use in the final index
I00 , and it allows us to say that, considering all
attributes, a municipality is above or below the
provincial average—and also by how much,
since it was measured in relative terms.
Nevertheless, if the final index is simply
I00 ¼ YW, then the attributes with greater variance
are overweighted. This effect can be seen in one
example. If in the Wealth Index a municipality
has a good position in four of the five attributes
but is badly placed in the other (perhaps due to
the fact that this attribute has a much higher
variance than the others), the final result will be
poor. In order to avoid this, we should compute
the index as:
I0 ¼ ZW;
where Zf are the standardized variables. We can
expect the variance of that index to be equal to
one. But if there is information common to these
attributes, we have:
VARðI0 Þ ¼ W0 RW;

CQLI ¼ 100 ð1 þ I  ½W 0 VARðYÞW Þ:

(3)

(4)

Any interpretation of this index has to be done
in relative terms. So, if CQLIi ¼ 102, municipality i is 2 % above the mean of the province in
terms of this component of quality of life. This
index, CQLIi, is the result of considering several
variables. Following the index structure
described in section 3, there are many attributes
that are summarized in 18 basic indexes. Then, in
order to reduce the number of components from
18 to only three, we simply have to adopt the
index structure again and then use this methodology, defining the weights of every component.
With three main quality of life components, we
do the same as before to obtain the final CQLI.
Then we can technically define the CQLI as
a weighted (a priori) arithmetic average index of
partial indicators that express the relative standardized position of every individual
(municipality, subsystem, or system) after
combining the variability of all variables, with
a Paasche-type temporal aggregation. To sum
up, the CQLI is an aggregate index that is
computed using partial information of every
dimension of quality of life.
Main results (see Fig. 1): the general improvement in quality of life throughout the province of
Barcelona. Nevertheless, the improvement was
far greater in some systems than in others.
Among the ones that recorded above average
rises were Anoia, Bages and Maresme Nord,
while Baix Montseny, Barcelona, Berguedà,

Objective Index of Quality of Life Developed for the Municipalities of the Barcelona Province

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Objective Index of Quality of Life Developed for the Municipalities of the Barcelona Province,
Fig. 1 Composite quality of life index at Barcelona’s municipalities

Garraf, and Maresme Nord rose less than
the average.
The movements in the three main components
explain these results. We can extract some
conclusions:
• The CQLI is higher in the pre-coastal strip, in
Osona, and in the city of Barcelona.
• The coastal strip has a lower CQLI than the
rest of systems and is lower than the inland
systems (Bages, l’Anoia or el Berguedà).

• The high IOP score explains the high general
CQLI result of the pre-coastal strip systems.
• Congestion in the coastal strip and in Barcelona, together with low scores on the Social
and Old Age Services Index, negatively
affected the QSLI of the coastal strip systems
and Barcelona.
• The provision of services is clustered in
Barcelona. This leads to high CCL indexes in
the city and in surrounding systems.

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This work was extended over the years until
2004, what resulted in a panel of quality of life for
more than 300 municipalities over 14 years. This
extensive database allowed for the development
of a list of academic works related with
the influence of quality of life at the urban scale
(see Royuela (2011), Faggian and Royuela
(2010), Royuela et al. (2010), Royuela and
Faggian (2009), Royuela et al. (2009), Royuela
et al. (2008), Royuela and Artı́s (2006) and
Royuela and Suriñach (2005).

Cross-References
▶ Objective Index of Quality of Life in Spain
▶ Objective Quality of Life

References
Blomquist, G. C., Berger, M. C., & Hoehn, J. P. (1988).
New estimates of quality-of-life in urban areas.
American Economic Review, 78, 89–107.
Dasgupta, P., & Weale, M. (1992). On measuring quality
of life. World Development, 20(1), 119–131.
Faggian, A., & Royuela, V. (2010). Migration flows and
quality-of-life in a metropolitan area: The case of
Barcelona-Spain. Applied Research in Quality
of Life, 5(3), 241–259.
Hirschberg, J. G., Maasoumi, E., & Slottje, D. J. (1991).
Cluster analysis for measuring welfare and quality of
life across countries. Journal of Econometrics,
50, 131–150.
Liu, B. C. (1973). Quality of life in the U.S., 1970. Kansas
City, KS: Midwest Research Institute.
Liu, B. C. (1978). Variations in social quality of life
indicators in medium metropolitan areas. American
Journal of Economics and Sociology, 37(3), 241–260.
Louis, A. M. (1976). The worst American city. In
A. Shank (Ed.), Political power and the urban crisis
(3rd ed., pp. 49–64). Boston: Holbrook Press, Inc.
Rogerson, R. J., Findlay, A. M., Morris, A. S., &
Coombes, M. G. (1989). Indicators of quality of life:
Some methodological issues. Environment and
Planning A, 21, 1655–1666.
Royuela, V., Surinach, J., Reyes, M. (2003). Measuring
quality of life in small areas over different periods of
time. Social Indicators Research, 64(1), 51–74.
Royuela, V. (2011). Modelling quality of life and population growth. The case of the Barcelona metropolitan
area. Spatial Economic Analysis, 6(1), 83–109.
Royuela, V., & Faggian, A. (2009). El crecimiento de las
ciudades y la calidad de vida en la provincial de

Objective Index of Quality of Life in Spain
Barcelona, desde una perspectiva de causalidad
mutua. Revista de Estudios Regionales, 85, 67–96.
Royuela, V., Moreno, R., & Vayá, E. (2010). The influence of quality of life on urban growth. A case study of
Barcelona. Regional Studies, 44(5), 551–567.
Royuela, V., Romanı́, J., & Artı́s, M. (2009). Using quality
of life criteria to define urban areas in Catalonia. Social
Indicators Research, 90(3), 419–440.
Royuela, V., & Suriñach, J. (2005). Constituents of quality
of life and urban size. Social Indicators Research,
74(3), 549–572.
Royuela, V., Suriñach, J., & Artı́s, M. (2008).
La influencia de la calidad de vida en el crecimiento
urbano. El caso de la provincia de Barcelona.
Investigaciones Regionales, 13, 57–84.
Royuela, V. & Artı́s, M. (2006). Convergence analysis in
terms of quality of life in the urban systems of the
Barcelona province in the period 1991–2000, Regional
Studies.
Slottje, D. (1989). Measuring the quality of life across
countries. The Review of Economics and Statistics,
73, 684–693.
Stover, M. E., & Leven, C. L. (1992). Methodological
issues in the determination of the quality of life in
urban areas. Urban Studies, 29(5), 737–754.

Objective Index of Quality of Life in
Spain
Coro Chasco
Economı́a Aplicada, Universidad Autónoma de
Madrid, Madrid, Spain

Synonyms
Quality of life in Spain

Definition
Concept and measurement of social welfare with
objective indicators

Description
Objective Index of Quality of Life in Spain
Measuring Social Welfare with Objective
Indicators
Quality of life is a complex concept involving
various socioeconomic domains, some of which

Objective Index of Quality of Life in Spain

can be measured objectively (e.g., income level,
access to health services, educational level)
whereas others are more a matter of people’s
subjective evaluations of happiness, health, or
social relationships. The 1991 Nobel Laureate
Amartya Sen defines social welfare as
a multidimensional concept that should not be
limited to disposable income but should include
other targets like education, security, health, and
even personal wishes. The Spanish National Statistics Institute similarly defines welfare as the
state of equal opportunities in which everyone
enjoys access to all “socially desirable” tangible
and intangible resources (INE, 2011).
Such a mixture of objective and subjective
components is not always easy to combine in
a single and exact cardinal measurement. This is
why Pena (1977) recommends finding a “useful”
though not necessarily exhaustive definition for
quality of life, which may be measured quantitatively so that comparisons may be made and
decisions taken. In the same vein, Zarzosa
(1996) proposes a composite indicator for
quality of life made up exclusively of objective
variables, that is, discarding those subjective
measures that might have different meanings in
terms of social welfare depending on people’s
culture or psychology. This kind of indicator is
what we call an “Objective Quality of Life Index”
(OQLI). An OQLI is a synthetic indicator
obtained from a set of objective variables,
which should have a plain meaning for the majority of the population in a country or region. It is
particularly useful in places with no single official data source for measuring quality of life. In
such instances, a key matter is the selection of the
relevant objective variables, which are provided
by various institutions and combined in a single
indicator.
Quality of Life Domains and Indicators Selection
Criteria

The selection criteria for an adequate set of
domains and variables of an OQLI should be
based on the guidelines of the main international
institutions, though the final selection of the variables will be very much constrained by data
availability. The list of suggested quality of life

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domains and indicators is long. A well-known
case is the Human Development Index (UNDP,
2013), which measures national development not
simply by national income, as had long been the
practice, but also by life expectancy and literacy.
The Spanish Office for Statistics provides a wide
range of social indicators classified by “fields of
social preoccupations”: family and social relations, education, work, income, distribution and
consumption, social protection, physical environment, culture and leisure, and social cohesion and
participation.
The OECD (2011) proposes differentiating
between two groups of indicators:
Social status indicators describe the general conditions of the population, that is, the social
outcomes that policies try to influence. These
variables are, in most cases, easily and unambiguously interpreted; for example, all countries would rather have low poverty rates than
high ones.
Societal response indicators include variables of
government policy settings, activities of
nongovernmental organizations, families, and
civil society more broadly. They provide
information about what society is doing to
affect social status indicators. By comparing
societal response indicators with social status
indicators, one can get an initial indication of
how effective a policy is.
Trends for both kinds of indicators must be
unambiguously interpreted as “good” or “bad”
for social welfare. It should be borne in mind
that any one indicator may have a different meaning depending on the spatial level or even the
period of time for which it has been extracted.
For this reason, confusing indicators should be
avoided as well as variables with narrow ranges.
For instance, a basic welfare indicator such as life
expectancy would not be appropriate for measuring quality of life across Spanish provinces in
recent decades, since differences are minimal.
In a recent analysis, Fundación “la Caixa”
(2004) built an OQLI for the Spanish regions in
line with OECD recommendations. It distinguishes twelve main domains relative to contemporary Spanish society, eight of which are
associated with social status (income, health,

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educational qualification, employment, housing,
civic involvement, public safety, and climate
and geography), while the other four are
societal response components (medical services,
culture and recreation, social protection, and
accessibility).
Using DP2 to Build an Objective Index of
Quality of Life

Constructing an OQLI involves combining the
different kinds of data available from different
statistical sources. The DP2 method or Pena’s
distance indicator is a method for constructing
synthetic global indicators from partial indicators
and is an alternative to the principal components
method. It consists basically in an iterative procedure that weights partial indicators according
to their correlation with a global index. In this
process, any redundant information that partial
indicators might contain will be eliminated
thereby avoiding multicollinearity problems.
The weights used are not subjective but depend
on the degree of correlation between each partial
indicator and the global index. Moreover, DP2 is
a cardinal measure, allowing comparisons
between units across space and/or time (Montero,
Chasco, & Larrraz, 2010; Pena, 1977; Somarriba
& Pena, 2008; Zarzosa, 1996).
The DP2 indicator takes as its point of departure a matrix V of order (k,m) where k is the
number of partial indicators and m the number
of spatial units. Each element of this matrix vkj
represents the state of the partial indicator k in the
spatial unit j. In this matrix, there would be both
positive elements (those positively correlated
with the variable under study) and negative elements (those with negative correlation).
We compute a distance matrix D where each
factor dkj is defined as dkj ¼ |nkjv*kj| where v*kj
is the kth component of the reference base vector
v*j ¼ {v*1j,. . ., v*kj} in the jth spatial unit. dkj
measures the distance between the partial indicator k at the spatial unit j and its reference value.
The most common practice is to consider the
minimal value as the reference one. As a result,
a higher DP2 value means a better level of social
welfare, since it implies a greater distance
from the minimum.

Objective Index of Quality of Life in Spain

In order to express every indicator in comparable abstract units, we use the Frechet distance:
DFðjÞ ¼

K
X
dkj
k1

sk

¼

K jv  v  j
X
_
kj
kj
;
s
k
k1

j ¼ 1; 2; . . . m
where sk is the standard deviation of partial
indicator k. The Frechet distance will be valid
only if every partial indicator is uncorrelated,
but in practice, this is not the case. Therefore,
a DF correction is used, that is, the Pena distance
or DP2, which is defined as:
DP2ðjÞ ¼

K
X
dkj
k1

sk

ð1  R2 k; k1; k2;...;1 Þ; j ¼ 1, 2; . . .; m

where R2k.k-1, k-2,. . .,1 is the determination coefficient of regression for each partial indicator k on
the others (k-1, k-2,. . .).
DP2 implies the decision about the partial
indicator’s order of entry into the computational
process. That is important because the value of
DP2 will be different depending on which
indicator comes first, which second, and so on.
Consequently, the partial indicator most closely
correlated with DF must be the leader, so it is the
most informative. From there, a ranking with
partial indicators is established according to
their degree of correlation with DF.
Once the first value for DP2, DP-1, is
obtained, we compute each partial indicator
correlation with DP-1 and we obtain a new
value for the DP2 index, DP-2. This process
must be repeated until convergence is reached.
Numerical values for DP2 are meaningless, but
they do allow us to rank the spatial units and to
make cardinal comparisons. They can be used to
compare changes and to detect their causes too.
An Objective Quality of Life Index (OQLI) for the
Spanish Regions

Next, we show the results of the estimation of an
OQLI for Spain’s NUTS-2 and NUTS-3 regions.
Following the guidelines of institutions like
UNDP, OECD, and INE, we selected an initial
set of 121 partial indicators, which were

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Objective Index of Quality of Life in Spain, Table 1 Ranking of the leading and trailing Spanish regions in the
OQLI and in its components (Spanish average ¼ 100)
Top CCAA

Bottom CCAA
Balearic Basque
CastileComponents Navarre Madrid Islands Country Catalonia Murcia Mancha
Social
139
135
130
127
127
89
88
welfare
Income
125
117
115
124
120
84
87
Health
110
106
89
105
96
85
113
Medical
150
129
113
133
109
83
82
services
Educational 152
141
99
149
125
80
67
qualif
Culture and 101
96
155
106
143
71
90
recreat
Employment 171
116
130
142
137
117
91
Social
100
143
98
86
101
138
82
protection
Housing
143
136
106
139
127
116
91
Accessibility 116
184
112
127
113
154
88
Civic
82
82
79
57
82
100
124
involvement
Public safety 75
88
84
28
92
97
115
Climate and
97
80
156
121
104
77
83
geography

unambiguously interpreted as “good” or “bad”
for social welfare. These indicators were aggregated by the DP2 method into 12 main welfare
domains: (1) income, (2) health, (3) medical
services, (4) educational qualification, (5) culture
and recreation, (6) employment, (7) social
protection, (8) housing, (9) accessibility,
(10) civic involvement, (11) public safety, and
(12) climate and geography. A subsequent combination of these 12 components with DP2 led to
the final OQLI. Since this index is a cardinal
measure, it is possible to rescale it as a new
variable, giving a value of 100 for the “Spanish
average.”
As can be seen from the Table 1, there is not
always a positive correlation between some “a
priori” complementary indexes, such as health
and medical services or employment and social
protection. This is the case of regions like the
Balearic Islands where, in spite of having
a good score in “medical services,” which is
a societal response domain, it has a belowaverage position in the social status component

Extremadura Galicia Andalusia
80
79
73
76
105
94

87
84
96

82
99
83

73

83

80

88

74

72

62
130

94
106

62
99

92
75
116

60
93
95

97
105
99

107
96

115
122

101
95

of “health.” Likewise, Extremadura is a region
with a low score for a social context component
such as “employment” but a better position in
“social protection,” which could be characterized
as a societal response welfare domain. These
results prove how important it is to consider
social status and societal response components
separately in order to ensure a better representation of social welfare in regional (intra-country)
indicators.

Cross-References
▶ Canadian Index of Well-Being
▶ Economic Well-Being
▶ Human Development Index (HDI)
▶ Index of Quality of Regional Development
▶ Internet and Quality of Life
▶ Objective Index of Quality of Life Developed
for the Municipalities of the Barcelona
Province
▶ OECD List of Social Indicators

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▶ Quality of Life Index
▶ Regional Quality of Life
▶ Spain
▶ Spanish Geography and the Quality of Life

Objective Quality of Life

subjective components. The objective quality of
life is restricted to the objective components or
dimensions and measured by objective indicators, which are statistics on social or societal
reality unfiltered by perceptions and independent
from personal evaluations.

References
Fundación “la Caixa”. (2004). Anuario social de
España 2004. Barcelona, Spain: la Caixa.
INE. (2011). Social indicators. Retrieved 31 May, 2012
from http://www.ine.es/
OECD. (2011). Society at a glance: OECD social indicators, 2011 edition. Retrieved 12 April, 2011 from
http://www.oecd.org/els/social/indicators/SAG
Montero, J. M., Chasco, C., & Larrraz, B. (2010). Building
an environmental quality index for a big city: A special
interpolation approach combined with a distance
indicator. Journal of Geographical Systems, 12(4),
435–459.
Pena, B. (1977). Problemas de medición del bienestar
y conceptos afines, (una aplicación al caso español).
Madrid, Spain: Instituto Nacional de Estadı́stica.
UNDP. (2013). Human development REPORT 2013, 20th
anniversary edition. Retrieved from http://hdr.undp.
org/en/reports/global/hdr2013/
Sen, A. (1991). Welfare, preference and freedom. Journal
of Econometrics, 50(3), 15–29.
Somarriba, J., & Pena, B. (2008). Quality of life and
subjective welfare in Europe: An econometric
analysis. Applied Econometrics and International
Development, 8(2), 55–66.
Zarzosa, P. (1996). Aproximación a la medición del
bienestar social. Valladolid, Spain: Secretariado de
Publicaciones e Intercambio Cientı́fico, Universidad
de Valladolid.

Objective Quality of Life
Jeroen Boelhouwer1 and Heinz-Herbert Noll2
1
The Netherlands Institute for Social
Research|SCP, The Hague, The Netherlands
2
Social Indicators Research Centre, GESISLeibniz Institute for the Social Sciences,
Mannheim, Germany

Definition
“Quality of life” is a normative conceptualization
of the good life and society which – depending on
its different notions – covers objective and/or

Description
Broadly speaking, there are two types of social
indicators to be used to measure and monitor the
quality of life, objective social indicators and
subjective social indicators, which are referring
to objective and subjective quality of life components. Though it can be debated where the borderline between the two actually is exactly
located, objective indicators are measures
informing about a factual situation (e.g., the size
of the house someone lives in), whereas subjective indicators are about an evaluation of that
situation (e.g., how satisfied one is with the housing space available). Though objective indicators
can be collected through registries (registered
number of visitors of a museum), they can also
be collected by asking people themselves (selfreported attendance of museums). Objective indicators may be measured at the individual level as
well as at the aggregated level.
The objective dimensions of quality of life can
cover a great variety of topics and are not necessarily limited to social circumstances, but may
rather include, for instance, environmental and
economic conditions too.
In the 1970s the objective indicator approach
within what is called the “social indicator movement” was particularly strong in the Scandinavian countries. Here the focus was clearly on
objective welfare or quality of life defined as
“the individual’s command over resources in the
form of money, possessions, knowledge, mental
and physical energy, social relations, security and
so on, through which the individual can control
and consciously direct his living conditions”
(Johansson in Erikson, 1993: p. 72). This
approach builds on the work of the economists
Jan Drewnowski (1974) and Richard Titmuss
(see Erikson, 1993) and is strongly related to

Objective Quality of Life

policy-making
purposes
(Vogel,
2002).
Drewnowski’s central argument was that wellbeing refers to the fulfillment of objectively identifiable needs, and Titmuss developed the idea
that the standard of living relates to the availability of resources. What matters, then, are objective
conditions, or life opportunities and their
determinants, not the evaluation of them
(Erikson, 1993). The Scandinavian “level of living” approach thus makes almost exclusively use
of objective indicators with a view to measure the
resources available to people and the conditions
they are living in.
The distinction between “resources” and
“conditions” as two aspects of the objective quality of life corresponds to some degree with the
distinction between individual and societal quality of life, the latter covering, e.g., “societal characteristics and qualities such as equality, equity,
freedom, or solidarity – which affect the welfare
situation of individuals at least indirectly” (Noll,
2002: 52). Issues of societal quality of life have
been addressed by some of the more recent concepts of well-being, such as social cohesion or
sustainability. Currently the association between
the level of inequality as a major societal quality
and individual quality of life is much debated
(Wilkinson & Pickett, 2010).
Indicators of objective quality of life overcome some of the “problems” of indicators of
subjective well-being. The latter are (at least partially) determined by people’s aspirations and the
degree to which people adapt to situations
(Allardt, 1993; Erikson, 1993). Moreover, doubts
have been expressed about the reliability (e.g.,
stability of observations) and validity (e.g., cultural bias) of subjective indicators. On the other
hand, in the view of critics of objective indicators – or supporters of the subjective approach –
“ultimately, the quality of life must be in the eye
of the beholder, and it is there that we seek to
evaluate it” (Campbell, 1972). In contrast to subjective indicators, the usage of objective indicators
starts from the assumption that living conditions
can be judged as being favorable or unfavorable
by comparing real conditions with external normative criteria like values or goals. An important
precondition of the possibility of making such

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judgments, however, is that there is political consensus first about the dimensions that are relevant
for welfare, second a consensus about good and
bad conditions, and third about the direction in
which society should move. This is of course
sometimes, but not always the case (Noll, 2000).
For a more detailed discussion on the
differences between objective and subjective
indicators, see, for instance, Rapley (2003), Noll
(2004), and Boelhouwer (2010).
Some approaches of measuring quality of life,
or well-being, only include objective measure,
whereas others include only subjective measures.
Particularly in the early days of quality of life
research, measurement approaches had a stronger
or even exclusive focus on the objective components. Nowadays it is more or less common practice to use both objective and subjective
indicators for quality of life measurement. Early
approaches which considered quality of life as
a combination of objective living conditions and
subjective well-being and thus making use of
objective and subjective measures were those
developed by Erik Allardt and Wolfgang Zapf.
Allardt
(1993)
famously
distinguished
three dimensions of welfare or quality of life –
“having,” “loving,” and “being” – and suggested
objective and subjective measures for each of
them. Zapf (1984) proposed a typology of the
relationship between subjective and objective
indicators distinguishing four different types of
quality of life (Table 1).
While the most desirable situation is the combination of good objective living conditions and
a high level of subjective well-being, the situations where bad-living conditions go together
with a high level of subjective well-being and
good living conditions with a low level of subjective well-being point to the so-called
Objective Quality of Life, Table 1 Four types of quality of life
Subjective well-being
Objective living
conditions
Good
Bad

High
Well-being
Adaptation

Source: Zapf, 1984, p. 25

Low
Dissonance
Deprivation

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satisfaction paradox and the satisfaction
dilemma, respectively.
Cummins (1997) suggests that the separate
measurement of objective and subjective components of quality of life is essential: “the contemporary literature is quite consistent in its
determination that, while both of these axes
form a part of the QOL construct, they generally
have a very poor relationship to one another.”
Overall, the current state of research suggests
that neither an exclusive focus at the objective
components nor a view limited to subjective
components allows to assess the quality of life
adequately and comprehensively. Rather it
seems to be the state of the art to include both –
objective and subjective dimensions – in quality
of life assessments and to relate the two to each
other as an important part of the analysis.

Cross-References

Objective Versus Subjective Value Theories
research (wider) of the united nations university
(pp. 67–84). Oxford: Clarendon.
Noll, H. H. (2000). Social Indicators and Social
Reporting: The International Experience. http://www.
ccsd.ca/noll1.html
Noll, H. H. (2002). Towards a European system of social
indicators: Theoretical framework and system architecture. Social indicators research 58 (pp. 47–87).
Dordrecht: Kluwer Academic Publishers.
Noll, H.-H. (2004). Social indicators and quality of life
research: Background, achievements and current
trends. In N. Genov (Ed.), Advances in sociological
knowledge over half a century (pp. 185–212). Wiesbaden: VS Verlag f€
ur Sozialwissenschaften.
Rapley, M. (2003). Quality of life research. A critical
introduction. London: Sage Publications.
Vogel, J. (2002). Strategies and traditions in Swedish
social reporting: A 30-year experience. Social indicators research 58 (pp. 89–112). Dordrecht: Kluwer
Academic Publishers.
Wilkinson, R., & Pickett, K. (2010). The spirit level: Why
equality is better for everyone. London: Penguin.
Zapf, W. (1984). Individuelle Wohlfahrt: Lebensbedingungen und wahrgenommene Lebensqualit€at. In
W. Glatzer & W. Zapf (Eds.), Lebensqualit€
at in der
Bundesrepublik, (pp.13–26). Frankfurt a. M. and
New York: Campus.

▶ Social Indicators
▶ Subjective Indicators
▶ Swedish Surveys of Living Conditions

Objective Versus Subjective Value
Theories
References
Allardt, E. (1993). Having, loving, being: An alternative to
the Swedish model of welfare research. In M. C.
Nussbaum & A. Sen (Eds.), The quality of life.
A study prepared for the world institute for development economics research (wider) of the United
Nations University (pp. 88–95). Oxford: Clarendon.
Boelhouwer, J. (2010). Wellbeing in the Netherlands. The
SCP life situation index since 1974. The Hague: The
Netherlands Institute for Social Research.
Campbell, A. (1972). Aspiration, satisfaction and fulfillment. In P. Converse & A. Campbell (Eds.), The
human meaning of social change (pp. 441–446).
New York: Russel Sage Foundation.
Cummins, R. A. (1997). Comprehensive quality of life
scale – fifth edition manual. Melbourne: Deakin University, School of Psychology.
Drewnowski, J. (1974). On measuring and planning quality of life. Mouton: The Hague: Institute of Social
Studies.
Erikson, R. (1993). Descriptions of inequality: The Swedish approach to welfare research. In M. C. Nussbaum
& A. Sen (Eds.), The quality of life. A study prepared
for the world institute for development economics

▶ Value Theories

Objective/Subjective Distinction
▶ Objectivity/Subjectivity of Values

Objectivity/Subjectivity of Values
Jason Raibley
Philosophy Department, California State
University, Long Beach, CA, USA

Synonyms
Objective/subjective distinction

Objectivity/Subjectivity of Values

Definition
In general, evaluative facts (e.g., the fact that
knowledge is good, the fact that stealing
is wrong) are called objective if they obtain
independently of the beliefs and other attitudes
(e.g., desires, approvals, hopes, wishes, fears,
likings) of subjects. By contrast, evaluative facts
are subjective if they depend for their existence
on the beliefs or attitudes of subjects.

Description
The objective/subjective distinction is deployed
in several related ways within the philosophical
and psychological literature on welfare,
▶ well-being, ▶ happiness, prudential value,
and ▶ quality of life (hereafter, “welfare”).
There is controversy about whether the welfare
of human beings and other sentient creatures is
itself objectively or subjectively good. More
prominently, there is a debate about whether the
true theory of welfare treats welfare as objective
or subjective. There is also considerable controversy concerning what makes theories objective
and subjective in the first place.
Objectivity and Subjectivity in Value Theory
In general, evaluative facts (e.g., the fact that
knowledge is good, the fact that stealing
is wrong) are called objective if they obtain
independently of the beliefs and other attitudes
(e.g., desires, approvals, hopes, wishes, fears,
likings) of subjects. By contrast, evaluative
facts are subjective if they depend for their
existence on the beliefs or attitudes of subjects.
For example, if knowledge is good simply in and
of itself, irrespective of whether people actually
do or would desire it, then the fact that knowledge
is good is objective – or equivalently, knowledge
is an objective good or value. By contrast, if what
makes stealing wrong is that certain people do
or would disapprove of it, then the relevant
evaluative fact is subjective – or equivalently,
stealing is subjectively bad.
Historically important ethicists can be
classified as objectivists or subjectivists

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depending on whether they hold that the most
important and fundamental evaluative facts are
objective or subjective. Plato, Aristotle, Henry
Sidgwick, G. E. Moore, and W. D. Ross are
usually classified as objectivists. Moore in
particular appears to hold a very strong form of
objectivism according to which evaluative facts
obtain independently of the very existence of
creatures with minds or psychologies (Moore,
1903). On the other hand, Hume, James, Nietzsche, and Dewey are normally classed as subjectivists. For such theorists, value and normative
reasons get into the world through the sentiments,
reactive attitudes, valuing activities, or purposes
of human beings. Other famous ethicists, such as
Hobbes, Kant, J. S. Mill, and Rawls, are more
difficult to classify, in part because there is controversy about the interpretation of their views
and in part because their views combine objective
and subjective elements.
Objective and Subjective Theories of Welfare
Theories of welfare can also be classified as
objective or subjective or as hybrids. There is
more agreement among welfare theorists about
which theories are objective and subjective than
about precisely why they count as such.
Versions of the Objective List Theory, perfectionism, Amartya Sen and Martha Nussbaum’s
capabilities
approach,
Richard
Kraut’s
developmentalism, and Daniel Haybron’s
self-fulfillment theory are all objective theories
of welfare (see Murphy, 2001, Hurka, 1993,
Nussbaum & Sen, 1993, Kraut 2007, Haybron,
2008). This is because they allow that at least
some conditions and activities are directly good
for one, whether or not one desires them, enjoys
them, takes satisfaction in them, or believes that
they are good. Conditions and activities thought
to have this status include knowledge, friendship,
love, moral virtue, the appreciation of beauty,
sensory awareness, mobility, and emotional
health.
By
contrast,
desire
satisfactionism,
preferentism, L. W. Sumner’s life satisfactionism,
Valerie Tiberius’s values-based theory, and Dale
Dorsey’s judgment subjectivism are subjective
theories of welfare (see Feinberg, 1984,

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Sumner, 1996, Tiberius, 2008, Dorsey, 2012). This
is because they say that in order for something to
be directly good for one, one must desire or prefer
or enjoy it, or else derive satisfaction from it, or
else believe that it is good.
▶ Hedonism is a more controversial case. Fred
Feldman’s attitudinal hedonism construes the
building blocks of welfare as episodes of enjoyment taken in propositional objects (Feldman,
2004). This form of hedonism resembles other
forms of subjectivism because it is based on personal attitudes; additionally, these attitudes may
be conceptually linked with desire (Heathwood,
2006). However, other forms of hedonism construe pleasure as an experiential state or a familyresemblance class of such states (Crisp, 2006).
These resemble the Objective List Theory, insofar as they claim that a particular experience is
good for one, no matter whether one desires it,
enjoys it, takes satisfaction in it, or believes it is
good. For this and other reasons, some theorists
have doubted hedonism’s subjectivist credentials
(Dorsey, 2011, Fletcher, forthcoming).
Other theories of welfare count as hybrids
insofar as they combine objective and subjective
elements. Of course, looked at in one way, most
of the objective theories already mentioned are
hybrids: for most of them allow that desire satisfaction, ▶ pleasure, or aim achievement is welfare-enhancing, but insist that other things like
knowledge and friendship are also welfareenhancing. Still, these are usually classed as
objective theories. Paradigmatic hybrid theories,
by contrast, require that the individual building
blocks of welfare each have subjective and objective elements. For example, Robert Adams proposes that welfare consists in the enjoyment of
things that are objectively excellent or worthwhile (Adams, 1999; cf. also Parfit, 1984; Griffin,
1986; Scanlon, 1998; Arneson, 1999; Feldman,
2004; Appiah, 2005). Other approaches, while
largely subjectivist, count as hybrids insofar as
they claim that the preservation of the systems
that make conation and goal-directed action possible is good for one (Raibley, 2012).
Before examining the reasons for thinking that
welfare must be either objective or subjective, let

Objectivity/Subjectivity of Values

us further consider the nature of this distinction.
Formulating it precisely has proven somewhat
difficult.
David Brink writes that “Subjective theories
of value claim that the components of a valuable
life consist in or depend importantly on certain of
the individual’s psychological states. . . . By contrast . . . objective theories of value claim that
what is intrinsically valuable neither consists in
nor depends importantly on such psychological
states” (1989, pp. 220–1). One worry with this
proposal is that “psychological states” form
a broad category. A theory that says that the
only welfare goods are knowledge and the
appreciation of beauty would effectively say
that the valuable life consists in psychological
states. But this would not be a characteristically
subjective theory.
L. W. Sumner has written that, according to
subjective theories, having a favorable attitude
towards one’s life or some of its ingredients is
a necessary condition for one’s life to be going
well for one (Sumner, 1996, p. 38). By contrast,
he says, objective theories allow that one could be
well-off without favorably regarding one’s own
life or any of its ingredients (p. 38). Sumner does
not provide both necessary and sufficient conditions for subjective theories, so this analysis is at
best incomplete. This necessary condition for
subjective theories may be approximately correct. However, it is not entirely clear that one
must have favorable attitudes towards the ingredients of one’s life to be faring well on some
forms of desire satisfactionism and aim
achievementism: if one is satisfying one’s desires
(or getting what one aimed for), it may not matter
that one does not enjoy (or is not satisfied with)
what one gets. Furthermore, most objective theories that have actually been defended do require,
at least for high levels of welfare, that one favorably regard aspects of one’s life.
Sobel (2009) recommends a different way of
distinguishing between objective and subjective
theories. He writes: “Subjective accounts of wellbeing maintain that one’s rationally contingent
non-truth-assessable pro-attitudes ground true
claims about what is good for one” (p. 336).

Objectivity/Subjectivity of Values

A problem is that this criterion may not correctly
classify versions of life satisfactionism and judgment subjectivism. This is because judgments
that one’s life is satisfactory or that one is faring
well do seem to be truth-apt. Some forms of
subjectivism base welfare on truth-assessable
pro-attitudes.
Dorsey proposes that subjectivism requires
that “prudentially valuable states be endorsed by
the person for whom these states are valuable”
(2011); he also writes that “subjectivism [states
that] a person’s evaluative perspective, under the
right conditions, determines that which is good
for her, and how good it is for her” (2013, p. 1).
While these formulations are suggestive and
plausible, it is a little unclear what endorsement
and a person’s evaluative perspective amount to.
On account of the difficulties noted in this
section, there may be no neat and precise way to
distinguish between objective and subjective theories of welfare. Perhaps this is to be expected: as
Fletcher notes, our taxonomies of welfare theories are interest relative, and so they are not likely
to reflect perfect joints in nature (Fletcher, 2012).
Perhaps if some of the building blocks or main
determinants of welfare are partly constituted by
pro-attitudes (desires, attitudinal pleasures, likings, values – perhaps also aims and intentions)
or by judgments of satisfaction or beliefs that
things are good for one, this is sufficient for
a theory to be partially subjective. Of course,
there are hybrid theories that are partially but
not wholly subjective, insofar as they say that
the contribution made by the building blocks of
welfare to the value of one’s life depends on the
objects of one’s pro-attitudes. Roughly speaking,
the more a theory says that one’s welfare level
depends on the objects of one’s pro-attitudes – or
on things besides one’s pro-attitudes, judgments,
and beliefs – the more objective the theory is.
Is Welfare Objective or Subjective?
Arguments for welfare’s objectivity aim to show
that subjective theories have unacceptable
implications about the welfare of individuals
who pursue trivial, worthless, masochistic, or
immoral ends. A person who simply desires – and

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enjoys – scratching an itch, counting blades of
grass, or knocking down icicles is surely not
faring well (Plato’s Philebus; Rawls, 1971,
Kraut, 1994). Those who aim for, achieve, and
enjoy great fame and wealth – or revenge upon
their enemies – do not seem to benefit proportionally (Kraut, 2007). A person who desires and
enjoys pain, bodily mutilation, and humiliation –
and gets all these things – is not normally
thought to be faring well (Carson, 2000;
Raibley, 2012). Finally, a person who desires
and enjoys inflicting harm on others does not
appear to be faring especially well.
But on the other hand, if a person does not like
or enjoy his life – and if he does not get anything
that he wanted or set out to achieve – it does not
seem that it can plausibly be called a good life for
him (cf. Adams, 1999, p. 95). And so it seems that
there is also some kernel of truth in the neighborhood of subjectivism.
Sumner famously argues that objective theories of welfare such as the Objective List Theory
and perfectionism fail to capture welfare’s “characteristically positional or perspectival character” (Sumner, 1996, p. 43). He concludes that
“subjectivity turns out to be a necessary condition
of success in a theory of welfare” (Sumner, 1996,
p. 27).
Sumner has several arguments for this conclusion. One, which we can call the weak argument,
claims that any plausible theory of welfare must
“make your well-being depend on your own
concerns: the things you care about, attach
importance to, regard as mattering, and so on”
(Sumner, 1996, p. 42). It is then claimed that
objective theories that accord no importance to
a subject’s hedonic and emotional states, conative attitudes, or judgments of satisfaction cannot
tie welfare to one’s own concerns in this way.
Therefore, such objective theories are unacceptable. This argument is persuasive, but it merely
establishes that pro-attitudes or beliefs of
the right sort be included among the direct determinants of welfare. But some objective and
hybrid theories do include these states (Arneson,
1999; Adams, 1999; Appiah, 2005; Fletcher,
forthcoming).

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A second argument can also be found in Sumner. The first premise of what we can call the
strong argument states the subject relativity of
welfare: “the prudential value of my life is
its value for me . . .” (p. 42). That is, welfare
value is a form of value for a subject, as opposed
to for the world or for mankind or for no one
in particular; it has a “characteristically positional or perspectival character” (p. 37, p. 43).
Since subjective theories of welfare say that welfare is largely or wholly constituted by perspectival attitudes – i.e., attitudes anchored in
a subject’s perspective – they afford the best
explanation of this fact: “welfare is subjectrelative because it is subjective” (p. 43). Accordingly, some subjective theory of welfare must be
true: we could not have an account of welfare’s
nature that made no reference to the subjective
experiences of the particular subject. This argument seems inconclusive. It might establish
that welfare does not turn entirely on nonexperiential properties of the subject. But whoever held that it did? Sumner seems to be
claiming that the positional or perspectival character of welfare value (the fact that it is value for
a subject) requires that welfare be given a strictly
subjective treatment. But it is not explained why
this is so (Sobel, 1997).
Another popular argument for subjectivism
about welfare turns on the internalism requirement (Rosati, 1996). This requirement states that,
if something, x, is good for a subject, S, then
S must be capable of being motivated to pursue
or promote x. Peter Railton explains the main
idea behind this requirement as follows: “[W]
hat is intrinsically valuable for a person must
have a connection with what he would find in
some degree compelling or attractive, at least if
he were rational and aware” (Railton, 2002,
p. 47). Some reason that if this requirement is
true, then some version of subjectivism is true.
However, the requirement itself is difficult to
interpret. What precisely is it to “be capable of
being motivated to pursue or promote” something? Additionally, there is a worry that this
use of the internalism requirement is questionbegging, because the requirement itself is
just subjectivism stated in another way. For

Objectivity/Subjectivity of Values

further discussion of these and related issues,
see Sarch, 2011.

Cross-References
▶ Ethics
▶ Eudaimonia
▶ Good Life, Theories of
▶ Happiness
▶ Preference Satisfaction Theories
▶ Well-Being, Philosophical Theories of

References
Adams, R. M. (1999). Finite and infinite goods. New
York: Oxford University Press.
Appiah, K. A. (2005). The ethics of identity. Princeton, NJ:
Princeton University Press.
Arneson, R. (1999). Human flourishing versus desire satisfaction. Social Philosophy and Policy, 16(1), 113–142.
Brink, D. (1989). Moral realism and the foundations of
ethics. Cambridge, UK: Cambridge University Press.
Carson, T. L. (2000). Value and the good life. South Bend,
Ind.: Notre Dame University Press.
Crisp, R. (2006). Reasons and the good. Oxford, UK:
Clarendon.
Dorsey, D. (2011). The Hedonist’s Dilemma. Journal of
Moral Philosophy, 8(2), 173–196.
Dorsey, D. (2012). Subjectivism without desire. Philosophical Review, 121(3), 407–442.
Feinberg, J. (1984). Harm to others. Oxford, UK: Oxford
University Press.
Feldman, F. (2004). Pleasure and the good life. Oxford,
UK: Clarendon.
Fletcher, G. (forthcoming). A fresh start for the objectivelist theory of well-being. Utilitas.
Griffin, J. (1986). Well-being. Oxford, UK: Clarendon.
Haybron, D. (2008). The pursuit of unhappiness. Oxford,
UK: Clarendon.
Heathwood, C. (2006). Desire satisfactionism and hedonism. Philosophical Studies, 128, 539–563.
Hurka, T. (1993). Perfectionism. Oxford, UK: Clarendon.
Kraut, R (2007). What is good and why. Cambridge, Mass:
Harvard University Press.
Moore, G. E. (1903). Principia Ethica. Cambridge, UK:
Cambridge University Press.
Murphy, M. C. (2001). Natural law and practical rationality. Cambridge, UK: Cambridge University Press.
Nussbaum, M., & Sen, A. (Eds.). (1993). The quality of
life. Oxford, UK: Clarendon.
Parfit, D. (1984). Reasons and persons. Oxford, UK:
Clarendon.
Raibley, J. (2012). Health and well-being. Philosophical
Studies, 161, 4.

Occupation Influence on Satisfaction/Happiness
Railton, P. (2002). Facts and values. In Facts, values, and
norms. Cambridge, UK: Cambridge University Press.
Rawls, J. (1971). A therory of jusice. Cambridge, Mass.:
Harvard Belknap.
Rosati, C. (1996). Internalism and the good for a person.
Ethics, 106, 297–326.
Sarch, A. (2011). Internalism about a person’s good:
Don’t believe it. Philosophical Studies, 154, 161–184.
Scanlon, T. M. (1998). What do we owe to each other?
Cambridge, MA: Belknap Press of Harvard University
Press.
Sobel, D. (1997). On the subjectivity of welfare. Ethics,
107, 501–508.
Sobel, D. (2009). Subjectivism and idealization. Ethics,
119, 336–352.
Sumner, L. W. (1996). Welfare, happiness, and ethics.
New York: Oxford University Press.
Tiberius, V. (2008). The reflective life. New York: Oxford
University Press.

Obligations
▶ Personal and Institutional Accountability

Observation Error
▶ Measurement Error

Occupation Influence on
Satisfaction/Happiness
Romana Pasca
Health Sciences, University of Northern British
Columbia, Prince George, BC, Canada

Synonyms
Job satisfaction; Occupational well-being

Definition
Occupation is defined as any activity, work, job,
line of work, or business in which a person is
engaged.

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Satisfaction is defined as a subjective feeling
of well-being and fulfillment of needs, interests,
or expectations.
Happiness is a complex and subjective concept
referring to a general mental state of well-being.

Description
Work and workplaces are important components
of people’s life and play a crucial role in the way
people perceive their overall ▶ well-being. There
is increased evidence linking work to satisfaction
and (Diener, Emmons, Larson, & Griffin, 1985).
Work, personal life, and family are strongly
interconnected to the extent that experiences in
one environment will inherently influence the
other environment. According to Kendall and
Muenchberger (2009), “workers are unlikely to
leave work pressures at work and, the pressures of
▶ family and community life are likely to impact
on perceptions of, or ▶ attitudes towards the job”
(p. 29).
Happiness is a multidimensional construct
with a complex definition. The most important
components of happiness refer to affective wellbeing (i.e., positive affects vs. negative affects),
▶ competence, meaning, satisfaction, and social
connectedness (Reho, Souto, & Pina e Cunha,
2009). Happy individuals report positive experiences and emotions that make them be more proactive and understanding, resilient to stressors,
and inclined to establish social ties, further translating into good ▶ health and well-being. Conversely,
individuals that report frequent experience of negative affects tend to have general negative views of
themselves, other people, and the world in general
and are less equipped to cope with stressors and
less willing to socialize and interact with others;
therefore, these individuals may be more prone to
experience ill mental health and chronic distress.
When translated into the work context, occupational satisfaction is defined as positive emotion and attitude towards one’s job. Increased
levels of occupational satisfaction are positively
associated with well-being and good mental
health (Faragher, Cass, & Cooper, 2005). The
core dimensions that contribute to high levels of

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occupational satisfaction are the nature of the job,
support at work, income, job control, and possibilities for advancement. As such, individuals
that report low levels of occupational satisfaction
are more likely to feel unhappy, frustrated, and
unfulfilled. Such negative emotions, over long
periods of time, can cause depression, anxiety,
or emotional exhaustion and spill over into family or social life.
In the context of work-family conflict, ▶ marital satisfaction plays an important role. Marital
satisfaction refers to the perceived overall quality
of the marital relationship (Sternberg & Hojjat,
1997). The happier and more supportive the marriage, the greater one’s feelings of competency
leading to more confident and positive attitudes
about job and life. Judge, Ilies, and Scott (2006)
demonstrated that when family issues prevent an
individual from fulfilling work expectations or
effectively completing work tasks, the individual
will experience high levels of frustration and
hostility at work.
Numerous theories have been developed
explaining the link between occupation and
well-being. Herzberg (1966) developed a twofactor theory of occupational satisfaction, taking
into consideration intrinsic and extrinsic factors
that influence satisfaction or dissatisfaction with
work. Intrinsic factors, such as feelings of
achievement, responsibility within the job, recognition for the job performed, or possibility of
advancement, refer to what a person does in their
job, while extrinsic factors, such as working conditions, remuneration, work relationship, and
support, refer to the work environment related
to that particular job. According to Herzberg,
these factors have a separate influence on perception of satisfaction or dissatisfaction within the
job. Intrinsic factors are suggested to play a more
important role regarding influence on job satisfaction, whereas extrinsic factors are suggested to
have greater effect on job dissatisfaction. The
workplace-based
effort-reward
imbalance
model, proposed by Siegrist (1996), postulates
that imbalance occurring between workplace
efforts and occupational rewards leads to dissatisfaction, unhappiness, and adverse health effects
(Van Vegchel, De Jonge, & Landsbergis, 2005).

Occupation Influence on Satisfaction/Happiness

Siegrist’s model emphasizes that work role has
the potential to provide opportunities of positive
self-experience, increased self-efficacy, and
recurrent positive experience of ▶ self-esteem if
one is adequately rewarded. Conversely, failure
of employers to recognize or reward efforts can
be detrimental to an employee’s health and wellbeing. Another theoretical framework largely
employed in the research is Caplan’s model
(Caplan, 1987; Caplan & Harrisson, 1993) that
looks at how well the person fits within his/her
environment (P-E). In the workplace context, the
term “fit” refers to the relationship existent
between the work personality of an individual
and the work environment. (Edwards &
Rothbard, 1999) stated that the “P-E fit theory
predicts that a perceived match between the person and environment is beneficial to mental and
physical well-being, whereas a perceived
mismatch signifies stress, produces mental and
physical strain (i.e., damage to well-being), and
stimulates efforts to resolve P-E misfit” (p. 87).
Most of the theoretical frameworks draw
attention to the fact that work and nonwork
aspects of an employee’s life and the possible
conflicts between work and nonwork can be
influenced by the level of occupational dissatisfaction and unhappiness perceived by the individual (Caplan & Harrisson, 1993). As such,
problematic work relationships (e.g., interpersonal conflict with coworkers/supervisors, inability to positively adjust to the requirements of the
job, lack of social support at work) are likely to
have a detrimental effect on the individual’s perceived satisfaction with respect to job content,
personal life, and marital relationship.
There is growing scientific evidence illustrating the fact that workplaces are becoming
increasingly demanding (Loretto et al., 2005).
An important factor when considering one’s satisfaction with his/her occupation and life is the
quality of ▶ social interactions within and outside of the workplace. Social support and social
ties prove to be reliable indicators of happiness
and satisfaction. For example, support from
coworkers and supervisors is associated with
high level of job and life satisfaction, enhanced
meaning from work, happiness, and well-being

Occupation Influence on Satisfaction/Happiness

(Beehr, Bowling, & Bennett, 2010; Rego & Pina
e Cunha, 2009). Conversely, lack of contact and
interaction with coworkers, hostility, harassment,
or injustice in the workplace are normally associated with negative health outcomes, increased
level of ▶ stress, and low levels of job and life
satisfaction. Kelloway and Day (2005) emphasize
the fact that social support may reduce the negative effects of workplace stressors by acting as
a buffer. These authors argue that social support
in the workplace represent an important component of the “new” model of work environments.
Healthy work environments encourage positive
interpersonal relationships at work, promote forgiveness in the workplace, and discourage workplace mistreatment (e.g., verbal abuse, work
obstruction, emotional neglect), which subsequently may positively impact psychological health
and mediate the conflict between work and health.

Conclusion
Satisfaction and happiness are subjective feelings
of well-being and refer to an individual’s perceived ▶ quality of life in relationship with their
personal standards (Kendall & Muenchberger,
2009). These experiences depend on an individual’s ability to perceive fulfillment and positive
emotions in any situation. In the context of
work, levels of satisfaction and happiness with
one’s occupation are influenced by a myriad of
factors, some related to the work environment –
the nature of work, job demand and job control,
job clarity, income, and advancement opportunities – and others related to individual’s characteristics, personality traits, coping skills, and
the quality of social interactions. As such, individuals with an optimistic view who experience
more positive emotions may not perceive work
and life conditions as deleterious, while individuals with a more pessimistic view who experience greater negative affectivity may tend to
exaggerate the stressful aspects of their work
and life conditions. In addition, when there is
a match between the characteristics and
demands of the occupation and the individual’s
personality and ability to fulfill these demands,

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individuals are happy and motivated by their
jobs, and a lower level of occupational dissatisfaction will be perceived. That is, workers holding “good” jobs, which “fit” with their needs,
interests, desires, backgrounds, and personality,
are more likely to believe that their work has
a positive effect on their health (Ettner &
Grzywacz, 2001).

Cross-References
▶ Anxiety
▶ Competence
▶ Health
▶ Job Satisfaction
▶ Motivation
▶ Negative Affect
▶ Occupational Health
▶ Person-Environment Fit Theory
▶ Positive Affect
▶ Quality of Life
▶ Resilience
▶ Self-esteem
▶ Well-Being

References
Beehr, T. A., Bowling, N. A. & Bennett, M. M. (2010).
Occupational stress and failure of social support:
When helping hurts. Journal of Occupational Health
and Psychology, 15(1), 45–59.
Caplan, R. D., & Harrison, R. V. (1993). Person environment fit theory: Some history, recent developments, and future directions. Journal of Social Issues,
49, 253–276.
Diener, E., Emmons, R. A., Larson, R. J., & Griffin, S.
(1985). The satisfaction with life scale. Journal of
Personality Assessment, 49, 71–75.
Edwards, J. R., & Rothbard, N. P. (1999). Work and
family stress and well – Being: An examination of
person – Environment fit in the work and family
domains. Organizational Behavior and Human Decision Processes, 77(2), 85–129.
Ettner, S. L., & Grzywacz, J. G. (2001). Workers’ perceptions of how jobs affect health: A social ecological
perspective. Journal of Occupational Health Psychology, 6(2), 101–113.
Faragher, E. B., Cass, M., & Cooper, C. L. (2005). The
relationship between job satisfaction and health:
A meta-analysis. Journal of Occupational and Environmental Medicine, 62, 105–112.

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Herzberg, F. (1966). Work and the nature of man. Cleveland: World Publishing.
Judge, T. A., Ilies, R., & Scott, B. A. (2006). Work-family
conflict and emotions: Effects at work an at home.
Personnel Psychology, 59, 779–814.
Kelloway, E. K., & Day, A. L. (2005). Building healthy
workplaces: What we know so far. Canadian Journal
of Behavioural Science, 37(4), 223–235.
Kendall, E., & Muenchberger, H. (2009). Stressors and supports across work and non-work domains: The impact on
mental health and the workplace. Work, 32, 27–37.
Loretto, W., Popham, F., Platt, S., Pavis, S., Hardy, G.,
MacLeod, L., et al. (2005). Assessing psychological
well-being: A holistic investigation of NHS employees.
International Review of Psychiatry, 17(5), 329–336.
Rego, A., & Pina e Cunha, M. (2009). Do the opportunities
for learning and personal development lead to
happiness? It depends on work-family conciliation.
Journal of Occupational Health Psychology, 14(3),
334–348.
Reho, A., Souto, S., & Pina e Cunha, M. (2009). Does the
need to belong moderate the relationship between perceptions of spirit of camaraderie and employees’ happiness? Journal of Occupational Health Psychology,
14(2), 148–164.
Siegrist, J. (1996). Adverse health effects of high effort –
Low reward conditions at work. Journal of Occupational Health Psychology, 1, 27–43.
Sternberg, R. G., & Hojjat, M. (1997). Satisfaction in close
relationship. New York: The Guildford Press.
Van Vegchel, N., De Jonge, J., & Landsbergis, P. A.
(2005). Occupational stress in (inter)action: The interplay between job demands and job resources. Journal
of Organizational Behavior, 26, 535–560.

Occupation: Job, Profession, Work,
Vocation
▶ Occupational Health

Occupational Attitudes
Filipa Castanheira
Nova School of Business and Economics, Nova
University of Lisbon, Lisbon, Portugal

Synonyms
Work attitudes

Occupation: Job, Profession, Work, Vocation

Definition
An “attitude” consists in a predisposition to
respond in a favorable or unfavorable way to
persons or objects in one’s environment (Steers &
Porter, 1991). Occupational attitudes can generally
be described as one’s evaluations, opinions, and
beliefs about certain facets of one’s job or about the
job as a whole.
An attitude includes three components:
1. Cognitive: The opinion or belief segment of an
attitude (what the worker thinks about. . .)
2. Affective: The emotional or feeling component
of an attitude (how the worker feels about. . .)
3. Behavioral component: An intention to behave
in a certain way toward someone or something
(How the worker intends to behave)
In the research literature, the three most extensively studied attitudes are job satisfaction, job
involvement, and organizational commitment.

Description
Since the beginning of the twentieth century, industrial psychologists have been seriously concerned
with the measurement, interpretation, and implications of job attitudes (Roethlisberger & Dickson,
1939). The Hawthorne studies raised the issue of
linking productivity and workplace attitudes, and
since then, it is generally assumed that the way
people behave at work is dependent on how they
feel about it.
An “attitude” consists in a predisposition to
respond in a favorable or unfavorable way to
persons or objects in one’s environment (Steers &
Porter, 1991). The three most frequently
studied occupational attitudes are job satisfaction, job involvement, and organizational
commitment.
Job satisfaction is “. . .a pleasurable or positive
emotional state resulting from the appraisal of
one’s job or job experiences” (Locke, 1976,
p. 1304). According to Spector (1997), job
satisfaction is the degree to which people like
their jobs. The measures of job satisfaction
can be faceted, whereby they measure various

Occupational Attitudes

dimensions of the job – while others are global –
or measure a single, overall feeling toward the job.
Two of the most widely validated measures of
facet satisfaction are the ▶ Job Descriptive Index
(JDI; Smith, Kendall, & Hulin, 1969) and the
Minnesota Satisfaction Questionnaire (MSQ;
Weiss, Dawis, England, & Lofquist, 1967).
Job involvement is “an individual’s psychological identification with a particular job”
(Kanungo, 1982, p. 342). It refers to the degree
to which a person identifies with a job, actively
participates in it, and considers performance
important to self-worth (Kanungo, 1982).
Kanungo (1982) explains that work and job
involvement are distinct constructs and states
that work involvement is a normative belief
about the value of work in one’s life, and it is
more a function of one’s past cultural conditioning or socialization, whereas job involvement is
a belief descriptive of the present job and tends to
be a function of how much the job can satisfy
one’s present needs (p. 324).
Kanungo (1982) has developed an instrument
to measure job involvement (JIQ). The questionnaire comprises 10 items measure in a 7-point
Likert scale.
Organizational commitment is the degree to
which an employee identifies with a particular
organization and its goals and wishes to maintain
membership in the organization. Although there
are several models to conceptualize organizational commitment, the most widely adopted is
the three-factor model proposed by Mayer and
Allen (1997).
Affective commitment is employees’ emotional attachment to, identification with, and
involvement in the organization.
Continuance commitment is the perceived
economic value of remaining with an organization compared to leaving it. This type of commitment is based on the costs that employees
associate with leaving the organization.
Normative commitment is an obligation to
remain with the organization for moral or ethical
reasons.
Allen and Meyer (1990) developed an instrument including 18 items based on three

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dimensions of organizational commitment –
affective,
continuance,
and
normative
commitment.
Several studies have established fairly high
correlations between the three job attitudes and
also similar patterns of relations between measures of these three attitudinal variables and
other job-related variables, raising serious concerns about the distinctiveness of the three constructs. In response to these doubts, Brooke,
Russell, and Price (1988) and Mathieu and Farr
(1991) developed empirical studies with the
three variables and provided evidence for the
discriminant validity of organizational commitment, job satisfaction, and job involvement
measures.
Work Attitudes’ Predictive Utility
Despite its intuitiveness, the “happy-productive
worker” thesis has generated a long debate in
organizational behavior literature. The potential
linkage between employee attitudes and performance was first considered as a result of the
Hawthorne studies (and the ensuing human relations movement) in the 1930s. Since then, it has
been extensively studied (Harrison, Newman, &
Roth, 2006; Judge, Thoresen, Bono, & Patton,
2001). Numerous meta-analyses (e.g., Harrison
et al., 2006; Judge et al., 2001) have demonstrated that positive job attitudes are accompanied by better work outcomes. Although the
existence of positive correlations has been well
established, the causal relationship between
job attitudes and performance was yet to be
demonstrated. More recently, Riketta (2008)
conducted meta-analytic regression analyses on
16 studies that had repeatedly measured performance and job attitudes. The results provide
some support for the common assumption that
job attitudes (both job satisfaction and commitment) influence in and extra-role performance.
Nevertheless, these effects were generally weak,
leading the author to advise “. . .researchers
should be cautious with practical recommendations such as increase job satisfaction or commitment to increase productivity” (Riketta,
2008, 479).

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Occupational Gender-Based Segregation

Cross-References
▶ Attitude Measurement
▶ Job Descriptive Index
▶ Job Satisfaction
▶ Motivation

References
Allen, N. J., & Meyer, J. P. (1990). The measurement and
antecedents of affective, continuance and normative
commitment to the organization. Journal of Occupational Psychology, 63, 1–18.
Brooke, P. P., Russell, D. W., & Price, J. L. (1988).
Discriminant validation of measures of job satisfaction, job involvement, and organizational commitment. Journal of Applied Psychology, 73(2), 139–145.
Harrison, D. A., Newman, D. A., & Roth, P. L. (2006).
How important are job attitudes? Meta-analytic comparisons of integrative behavioral outcomes and time
sequences. Academy of Management Journal, 49,
305–325.
Judge, T. A., Thoresen, C. J., Bono, J. E., & Patton, G. K.
(2001). The job satisfaction-job performance relationship: A qualitative and quantitative review. Psychological Bulletin, 127, 376–407.
Kanungo, R. (1982). Measurement of job and work involvement. Journal of Applied Psychology, 67(3), 341–349.
Locke, E. A. (1976). The nature and causes of job satisfaction. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 1297–1349).
Chicago: Rand McNally.
Mathieu, J., & Farr, J. (1991). Further evidence for the
discriminant validity of measures of organizational
commitment, job involvement, and job satisfaction.
Journal of Applied Psychology, 76(1), 127–133.
Meyer, J. P., & Allen, N. J. (1997). Commitment in the
workplace. Thousand Oaks, CA: Sage.
Riketta, M. (2008). The causal relation between job attitudes and performance: A meta-analysis of panel studies. Journal of Applied Psychology, 93(2), 472–481.
Robthlisbeeger, E. J., & Dickson, W. J. (1939). Management and the worker. Cambridge: Harvard University
Press.
Smith, P. C., Kendall, L., & Hulin, C. L. (1969). The
measurement of satisfaction in work and retirement:
A strategy for the study of attitudes. Chicago: Rand
McNally.
Spector, P. E. (1997). Job satisfaction: Application,
assessment, causes, and consequences. Thousand
Oaks, CA: Sage.
Steers, R. M., & Porter, L. W. (Eds.). (1991). Motivation
and work behavior (5th ed.). New York: McGraw-Hill.
Weiss, D. J., Dawis, R. W., England, G. W., & Lofquist,
L. H. (1967). Manual for the Minnesota satisfaction
questionnaire. Minneapolis: University of Minnesota
Industrial Relations Center, Work Adjustment Project.

Occupational Gender-Based
Segregation
▶ Occupational Sex Segregation

Occupational Health
Romana Pasca
Health Sciences, University of Northern British
Columbia, Prince George, BC, Canada

Synonyms
Health: well-being, fitness, stamina, strength;
Occupation: job, profession, work, vocation;
Occupational: related to work or profession

Definition
Occupational health is a cross-disciplinary area
that refers to prevention, promotion, and maintenance of health, safety, and well-being of individuals engaged in the workplace. Being an
interdisciplinary field, occupational health draws
upon and synthesizes knowledge from many disciplines, such as public health, occupational hygiene,
occupational psychology, ergonomics, chemistry,
medicine, and disability management.

Description
The concept of occupational health was
first defined in 1950 as a result of a joint collaboration between the International Labour Organization (ILO) and ▶ World Health Organization
(WHO). These two organizations agreed that
“▶ occupational health should aim at: the
promotion and maintenance of the highest degree
of physical, mental and social ▶ well-being of
workers in all occupations; the prevention amongst
workers of departures from health caused by their
working conditions; the protection of workers in

Occupational Health

their employment from risks resulting from
factors adverse to health; the placing and
maintenance of the worker in an occupational
environment adapted to his physiological and
psychological capabilities; and, to summarize,
the adaptation of work to man and of each man
to his job” (International Labour Organization
[ILO] /World Health Organization [WHO]
1995).
However, the concept of ▶ occupational
health has its roots very early in history (Reese,
2003). Ancient Egyptians, aware of the negative
impact on ▶ health associated with fumes
resulting from the manufacturing of gold and
silver, were the first ones to develop a first aid
manual for workers. Around 1750 B.C., Hammurabi, a king of Babylon famous for the first
written codes of law, created a compensation system by placing a monetary value on permanent
injuries. Subsequently, the father of medicine,
Hippocrates, noted the presence of respiratory
health issues in stone cutters, while Romans,
who also observed risks associated with working
conditions, appealed to a goddess, Salus, to protect the health and safety of laborers and slaves.
However, it was only around the time of the
Industrial Revolution in the 1800s, when technological innovations and industrial machineries
were introduced to replace most of the manual
labor, that an increased awareness about work
environmental hazards and job-related injuries
and illnesses started to be firmly entrenched in
society. Mining was the first industry to consider
regulations regarding health and safety procedures in the workplace; soon after, other
industries followed by enacting safety acts and
regulations (Reese, 2003). In 1884, Germany was
the first country in the world to adopt a legislation
supporting workers’ compensation and also to
endorse occupational health and safety
regulations within the workplace.
The Canadian Context
In Canada, the advent of workers’
compensation legislation and regulations started
appearing early in the 1900s (Hansen, 1991).
Ontario led the way by introducing workplace
safety legislation in 1915, followed shortly

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thereafter by Manitoba and British Columbia.
Despite the fact that each province has its own
regulation system, the provincial ▶ Workers’
Compensation Acts comply with Canada Labour
Code and represent the standard legal framework
that must be followed by both employers and
workers (Gunderson & Hyatt, 2000). Due to the
“historic compromise,” workers that become
injured in the course of employment are to be
compensated for lost wages and cost of medical
care, in exchange releasing the employer from
liability. The workers’ compensation system is
an insurance-based system in which the right of
an injured employee to sue for the tort of
negligence is exchanged by the wage replacement and medical benefits covered by the insured
employer. However, the employer has the
responsibility to ensure that the workplace
standards comply with the Occupational Health
and Safety Regulations. These regulations are
adopted under each province ▶ Workers’ Compensation Act and contain mandatory regulations
and standards that must be met by all workplaces.
These standards have the authority of law
(Reese, 2003).
Emerging Issues
Following the recommendations of the Occupational Health and Safety regulations, the overall
goal of ▶ occupational health is to recognize and
prevent work-related diseases and injuries in the
workplace. The main areas covered by these
regulations relate to the identification and
analysis of workplace hazards, identification of
occupational illnesses or injuries, intervention,
control, and prevention of workplace injury and
illness, as well as safety and health training interventions in the workplace (Reese, 2003).
However, the scope of occupational health goes
beyond protecting just individuals engaged in the
workplace, such as workers and coworkers, to
also protecting all other parties that may be
affected by the work environment, such as family
members, customers, ▶ public health, and
▶ community. As such, in the recent years, due
to increase awareness about the negative impact
of the industry on the environment, as well as
about the link between the environmental factors

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and human health, industrial hygiene has
emerged as a new area of interest in the occupational health and safety domain (Sullivan, 2000).
Industrial hygiene has become very instrumental
in analyzing and determining prevention
measures in regard to minimizing the impact of
workplace environment on human health.
However, as the labor force has changed, due
to the technological innovation, globalization,
and the swing from the manufacturing, forestry,
and mining industries to the resource service
sector, occupational health perspectives have
changed as well (Sullivan, 2000). Under the
recent economic development and design of
work environments, the scope of occupational
health expands from more traditional occupational health and safety issues to a new and
more holistic approach taking into consideration
not only the physical, psychological, and social
well-being of the participants in the workplace
but also the overall organizational health (Levy &
Wegman, 1988; Naumanen & Liesivuori, 2009).
Thus, workplace security, respect of ▶ equality,
and diversity in the workplace, clear organizational
goals, transparency, and good communication are
new and emerging topics within the occupational
health and safety field. In a study regarding healthy
workplaces, Kelloway and Day (2005) noted that
“holistic workplace health includes physical,
social, personal, and developmental organizational
support to improve overall employee quality of life
both within and outside the workplace.” (p. 224)
In concert with the occupational health and
safety regulations, workplace health promotion
recognizes the importance of fostering positive
attitudes and improving employees’ ▶ satisfaction and engagement in the workplace. The combined efforts and ▶ motivation of employers,
employees, and communities to promote health
and well-being within the workplace are more
likely to further translate in organizational success and community well-being (Arandjelovic,
2011; Puchalski, Korzeniowska, Pyzalski, &
Wojtaszczyk, 2005). Therefore, the modern
understanding of the concept of workplace health
promotion advocates for a profound change at all
levels of interaction, employees, organization,
employers, family, and community in order to

Occupational Health

improve and facilitate positive changes on health
and well-being (Health Canada, 2004).
Conclusion
Despite the fact that the history of the occurrence
of work-related illness and injuries has its roots in
ancient times, only in recent history has
this awareness been channeled into a more considerate approach for workers’ ▶ health and
▶ well-being (Hansen, 1991). ▶ Occupational
health has come a long way from its beginnings
when it was very much focused on the health of
individuals working preponderantly in the
manufacturing and heavy industry sector.
Today, occupational health has broadened its
mandate and has taken a permanent place of
high priority within the workplace environment.
Occupational health and safety has become an
integral component of the way workplaces are
managed, organized, and regulated in order to
provide a safe and healthy work environment to
all participants in the work process. It represents
an important component of any organization
and has a significant impact on every worker
irrespective of the workplace, size of the
company, or field of activity.

Cross-References
▶ Health
▶ Job Satisfaction
▶ Meaningfulness of Work
▶ Motivation
▶ Occupational Health
▶ Public Health
▶ Quality of Life
▶ Well-Being
▶ Workers’ Compensation

References
Arandjelovic, M. (2011). A need for holistic approach to
the occupational health developing (in Serbia). International Journal of Occupational Medicine and Environmental Health, 24(3), 229–240.
Gunderson, M., & Hyatt, D. (2000). Workers’ Compensation: foundations for reform. Toronto/Buffalo,
New York/London: University of Toronto Press.

Occupational Mobility
Hansen, D. J. (1991). The work environment: occupational health fundamentals. Michigan, IN: Lewis
Publishers.
Health Canada. (2004). About healthy workplaces.
Retrieved September 23, 2011 from http://www.nqi.ca/
HealthyWorkplace/default.aspx
ILO/WHO. (1995). Joint convention. Retrieved September
23, 2011, from http://www.ilo.org/safework_bookshelf/
english?content&nd¼857170174
Kelloway, E. K., & Day, A. L. (2005). Building healthy
workplaces: What we know so far. Canadian Journal
of Behavioural Science, 37(4), 223–235.
Levy, B. M., & Wegman, D. H. (1988). Occupational
health: Recognizing and preventing work-related disease (2nd ed.). Boston/Toronto: Little, Brown and
Company.
Naumanen, P., & Liesivuori, J. (2009). Workplace health
promotion activities of Finnish occupational health
nurses. Public Health Nursing, 26(3), 218–228.
Puchalski, K., Korzeniowska, E., Pyzalski, J.,
& Wojtaszczyk, P. (2005). Opinions of Polish
occupational medicine physicians on workplace
health promotion. International Journal of Occupational Medicine and Environmental Health, 18(3),
241–249.
Reese, C. D. (2003). Occupational health and safety management: A practical approach. Boca Raton, FL/London/New York/Washington, DC: Lewis Publishers.
Sullivan, T. (2000). Injury and the new world of work.
Vancouver/Toronto: UBC Press.

Occupational Illness
▶ Workers’ Compensation

Occupational Mobility
Jeroen van der Waal1 and Willem de Koster2
1
Department of Sociology, Erasmus University
Rotterdam, Rotterdam, The Netherlands
2
Centre for Rotterdam Cultural Sociology
(CROCUS), Erasmus University Rotterdam,
Rotterdam, The Netherlands

Synonyms
Class mobility; Downward and upward mobility;
Social mobility; Upward mobility

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Definition
Transition from one occupation to another, either
during one’s own life course (“intragenerational
mobility”) or compared to one’s parents
(“intergenerational mobility”). A distinction is
made between transitions between occupations
of different hierarchical levels (“vertical mobility”) and changes between occupations of the
same level (“horizontal mobility”). The scholarly
literature overwhelmingly focuses on the former
type of occupational mobility, using better working conditions, and increases in occupational
prestige, remuneration, and authority as indicators of upward mobility.

Description
Occupational Mobility
A large number of studies in the social sciences
have been devoted to the issue of social mobility.
Many of these focus on the extent to which social
mobility takes place and on the question of how it
relates to industrial and postindustrial development; various scholars consider the level of social
mobility to be an indicator of societal “openness”
(Matras, 1980). Social mobility generally refers
to a transition from one socioeconomic position
to another: someone is socially mobile if she/he
transcends the boundaries of the class structure of
a society. Social mobility hence boils down to
class mobility, and notwithstanding the various
class definitions used in the social sciences at
large, which are rooted in distinct theoretical
traditions (Wright, 2005), the most widely used
class categorizations in recent decades build on
the occupational ladder (cf. Van der Waal,
Achterberg, & Houtman, 2007). Class position,
then, simply reflects ones occupation, and in most
studies social mobility or class mobility hence
indicates occupational mobility. Scholars agree
that almost all western countries experienced rising levels of upward mobility in recent decades,
while the exact extent to which remains contested
(Breen, 2004).
Studies on occupational mobility typically
deal with changes in objective class positions

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instead of transformations in subjective class
identifications. Commonly, an analytical distinction is made between “intergenerational mobility,” in which case people’s class positions differ
from those of their parents, and “intragenerational mobility,” which denotes mobility from
one class to another within one’s life span. Occupational mobility can both be upward and downward. In a simplified two-class model, in which
a “working class” is positioned below a “middle
class,” the upwardly mobile moves from the working class to the middle class, while it is the other
way around for the downwardly mobile.
The empirical literature devoted to the consequences of occupation mobility is vast – ranging
from ▶ voting behavior (Nieuwbeerta, 1996) to
▶ quality of life. The latter literature is primarily
inspired by sociologists Durkheim and Sorokin
(see for a recent overview: Houle, 2011), who
stressed the potential detrimental effects of “personal or societal disequilibrium, because of the
▶ stress it supposedly induces” (Marshall &
Firth, 1999, p. 31). The underlying idea is that
the occupationally mobile – be it upwardly or
downwardly – is uprooted from his/her familiar
social surroundings and has a hard time getting
accustomed to the patterns of thinking, feeling,
and acting that are characteristic of their new
situation. As a result, feelings of ▶ anxiety, insecurity, and confusion about their identity put
strain upon their ▶ subjective well-being (idem).
Such “pessimistic” accounts are often
counterposed to ones that build on research that
has shown time and again that more privileged
economic positions are accompanied by higher
levels of self-reported ▶ quality of life: level of
▶ education, income, and employment all correlate positively with ▶ subjective well-being
(Argyle, 1999). From these findings, it follows
that upward occupational mobility entails an
increase in ▶ subjective well-being: such mobility is, after all, often accompanied by a higher
level of ▶ education and an increase in income,
and for some it will mean becoming employed.
Those who experience downward occupational
mobility, in contrast, often see their level of
income decrease, and they may even become
unemployed. Hence, it is to be expected that

Occupational Mobility

their ▶ subjective well-being declines. Furthermore, besides these “hard” economic corollaries
of occupational class position, three other factors
underlie higher levels of subjective well-being:
a rich social life and more active ▶ leisure activities, high status, and greater ▶ job satisfaction
(Argyle, 1999).
While both accounts suggest that downward
occupational mobility induces a lower ▶ quality
of life, two contrasting expectations can be
discerned if upward occupational mobility is
concerned. Whereas the literature on social
uprooting predicts a decrease in ▶ subjective
well-being due to upward mobility, studies on
the corollaries of privileged economic positions
suggest that upward mobility results in an
increase in well-being. Thus far, the empirical
literature remains inconclusive about the empirical tenability of both expectations. It is reported
that occupational mobility is not “associated with
▶ psychological distress and self-acceptance”
(Houle, 2011, p. 757). And if occupational mobility is related to indicators of ▶ subjective wellbeing of a similar kind, it is found that “the
estimated effects – even if accepted as real – are
very small” (Marshall & Firth, 1999, p. 44; see
for a recent overview Veenhoven, 2011), and it is
argued that these “do not merit further investigation” (idem).
Yet, the latter conclusion might well be invalid if upward occupational mobility is concerned.
This is because empirical research has not simultaneously modeled both expectations outlined
above. It might, after all, well be that the absence
of a relationship between upward occupational
mobility – even if found in a multivariate analysis
including various control variables – is the
result of a cross-pressure effect (Lazarsfeld,
Berelson, & Gaudet, 1972 [1944], p. 53) because
both expectations are empirically valid. In that
case, a net effect of upward occupational mobility
on ▶ subjective well-being that is close to zero
might in fact represent the sum of a negative
effect due to a process of social uprooting and
a positive effect of acquiring a more privileged
economic position, a richer social life, a higher
status, and/or a higher level of ▶ job satisfaction.
Future research should, therefore, explicitly

Occupational Sex Segregation

model both mechanisms in order to uncover
whether upward occupational mobility is really
virtually unrelated to changes in subjective
▶ quality of life.

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Occupational Segregation by Gender
▶ Occupational Sex Segregation

Cross-References

Occupational Segregation by Sex
▶ Anxiety
▶ Class Identification
▶ Education
▶ Job Satisfaction
▶ Leisure
▶ Psychological Distress
▶ Quality of Life
▶ Stress
▶ Subjective Well-Being
▶ Voting Behavior

References
Argyle, M. (1999). Causes and correlates of happiness. In
D. Kahneman, E. Diener, & N. Schwarz (Eds.), Wellbeing: The foundations of hedonic psychology
(pp. 353–373). New York: Russell Sage.
Breen, R. (Ed.). (2004). Social mobility in Europe.
Oxford: Oxford University Press.
Houle, J. N. (2011). The psychological impact of intragenerational social class mobility. Social Science
Research, 40(3), 757–772.
Lazarsfeld, P. F., Berelson, B., & Gaudet, H. (1972
[1944]). The people’s choice: How the voter makes
up His mind in a presidential campaign. New York:
Columbia University Press.
Marshall, G., & Firth, D. (1999). Social mobility and
personal satisfaction: Evidence from ten countries.
The British Journal of Sociology, 50(1), 28–48.
Matras, J. (1980). Comparative social mobility. Annual
Review of Sociology, 6, 401–431.
Nieuwbeerta, P. (1996). The democratic class struggle in
postwar societies: Class voting in twenty countries,
1945–1990. Acta Sociologica, 39(4), 345–383.
Van der Waal, J., Achterberg, P., & Houtman, D. (2007).
Class is not dead – It has been buried alive. Class
voting and cultural voting in postwar western societies
(1956–1990). Politics & Society, 35(3), 403–426.
Veenhoven, R. (2011). Finding on happiness and social
mobility. In: R. Veenhoven (ed.), World Database
of Happiness. Rotterdam: Erasmus University
Rotterdam, Accessed October 25, 2011 at: http://
worlddatabaseofhappiness.eur.nl/hap_cor/top_sub.php?
code¼S5
Wright, E. O. (Ed.). (2005). Approaches to class analysis.
Cambridge/New York: Cambridge University Press.

▶ Occupational Sex Segregation

Occupational Sex Segregation
Olga Alonso-Villar and Coral Del Rı́o
Departamento de Economı́a Aplicada,
Universidade de Vigo, Vigo, Pontevedra, Spain

Synonyms
Occupational gender-based segregation; Occupational segregation by gender; Occupational
segregation by sex; Sex segregation of
occupations

Definition
Following (Reardon & O’Sullivan, 2004), “▶ segregation can be thought of as the extent to which
individuals of different groups occupy and experience different social environments.” To put it more
sharply, “segregation is a social mechanism that
preserves inequality across groups” (Reskin,
1999). Although different dimensions of the
problem have been described in the literature
(Massey & Denton, 1988), evenness is the most
frequently discussed. According to this conception, segregation exists if the population subgroups
into which the economy is divided (women/men,
blacks/whites) are not similarly distributed among
organizational units (occupations, schools,
neighborhoods). In particular, occupational sex
segregation occurs when women are underrepresented (or overrepresented) in some occupations as
compared to men. The most extended view of
occupational segregation—that of horizontal

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segregation—does not consider occupations as
being ordered by any criterion. As opposed to
that, vertical segregation arises when women are
underrepresented in high-status occupations,
especially those related to decision making.

Description
As documented by (Anker, 1998), there is ▶ gendered work all over the world. In most countries,
some occupations are strongly masculinized
(architects, engineers, and related technicians;
legislative officials and government administrators; managers; sales supervisors; protective
service workers; production supervisors and
general foremen; blacksmiths; bricklayers,
carpenters, and other construction workers),
while others, most of them low paid, tend to be
female dominated (nursing; stenographers, and
typists; housekeepers, bookkeepers/cashiers,
building caretakers/cleaners, and tailors/sewers).
Sociologists and economists have devoted
a great deal of attention to measuring occupational sex segregation. Most of these measures
are based on comparisons between the distribution of women across occupations and that of men
(for a survey, see Fl€uckiger & Silber, 1999),
though a few measures contrast instead the
former against the occupational structure of the
economy (Del Rı́o & Alonso-Villar, 2010;
Moir & Selby Smith, 1979). Among occupational
segregation measures, the index of dissimilarity
(ID) proposed by (Duncan & Duncan, 1955)
has been one of the most widely used to quantify
horizontal segregation, including trends in a large
number of countries, in some cases combining
sex with race and ethnicity (for recent studies,
see Anker, Malkas, & Korten, 2003; European
Commission & Employment, Social Affairs and
Inclusion, 2009; Kaufman, 2010; for earlier studies, see Albelda, 1986; Blau, Simpson, & Anderson, 1998; King, 1992; Reskin, 1999). The value
of this index, which can range between 0 and 100,
can be interpreted as the percentage of workers
who would have to change their occupations to
bring about a similar occupational distribution for
women and men.

Occupational Sex Segregation

It is worth noting that, on the one hand, the
segregation measurement does depend on
the level of aggregation of occupations, so that
the finer the classification of occupations being
used, the higher the extent of segregation. On the
other hand, as pointed out by (Baron, 1994),
official classifications may include asymmetries
in the treatment of occupations of female- and
male-dominated occupations, the classification of
the latter being usually finer than that of the
former, which affects the segregation measurement as well.
In order to offer empirical evidence of the ID
index, we include here some figures for the
European Union and the United States. In the
EU, the ID index in 2007 was around 51 %
(European Commission & Employment, Social
Affairs and Inclusion, 2009), which means that,
to obtain similar distributions for women and men,
more than half of workers would have to change
occupations. By using a different classification
that considers a list of 104 occupations rather
than 130, the ID index in the USA in 2000 was
lower: around 46 % (Anker et al., 2003) (although,
as mentioned above, one must be careful in deriving conclusions from cross-country comparisons
when different classifications are involved).
Discussion
▶ Gender inequalities in the labor market may
arise from several sources, including individual
characteristics, market opportunities, and
environmental conditions. From an economic
perspective (see Altonji & Blank, 1999 for
a survey of neoclassical theories), discrepancies
between the jobs filled by women and men may
be the result of differences in education and
experience, as explained by ▶ human capital
theory. Disparities may also occur as
a consequence of gender differences in job attribute preferences, although these differences are
certainly not independent of social conditions
to the extent that women assume most of the
domestic responsibilities (including child and
elderly care), which explains why some women
prefer occupations with flexible work hours.
Empirical evidence shows, however, that the
overrepresentation of women in some types of

Occupational Sex Segregation

jobs can be explained not only by supply-side
factors (such as differences in preferences or productivity) but also by demand-side factors,
including ▶ gender discrimination (Kaufman,
2010; Petrongolo, 2004). In explaining sex
segregation, neoclassical discrimination theories
focus on the assumption of a taste for discrimination on the part of employers, coworkers, and
consumers, as well as on stereotyping, considering that employers often assess workers
according to the average characteristics of the
group they belong to rather than to their actual
characteristics (statistical discrimination), which
in practice results in ▶ negative stereotypes
against women. All this can be reinforced by
workers’ actions when achieving the characteristics of the job they are offered.
The above neoclassical theories have been
criticized from a feminist perspective, mainly
because they do not provide meaningful explanations for the origin of discrimination while
assuming that individuals make free, rational
choices. For these reasons, feminist scholars
have considered alternative economic traditions
together with approaches from other fields
(Burnell, 1999). Thus, according to segmented
labor market theories, the preponderance of
women in some kinds of occupations may arise
from the separation of the whole labor market
into primary and secondary markets which differ
in their rewards, status/authority, unionization,
physical exertion/dexterity, and employment
opportunities. Queuing labor processes also
fuels segregation because applicants to a job are
ranked not only by their skills but also by sex,
which leads to different labor queues for women
and men, the former being relegated to less desirable jobs.
From recent approaches, segregation may be
also viewed as a way to deal with the difficulties
that occur when women and men work side by
side in the same workplace. As pointed out by
(Goldin, 2002), men lose status when women are
hired for the same jobs they hold since integration
can be seen as a sign that those occupations have
low requirements. Consequently, discrimination
against women may emerge as a form of
protection of men’s occupational status. Akerlof

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and Kranton (2000) adds further psychological
considerations to the analysis: When a woman
works in what has traditionally been a male occupation, she suffers a direct cost due to the loss of
her female identity and an indirect (but not necessarily lower) cost if the identities of her male
coworkers are affected by her presence and they
act to strengthen and maintain their own identities. This results in separation between women
and men at work.
There are many reasons why researchers
and policy makers care about occupational
segregation by sex (Anker, 1998; Cohen &
Huffman, 2003; European Commission, Employment, Social Affairs and Inclusion, 2009;
Kaufman, 2010):
1. Approximately one-third of the salary
differences between women and men at the
international level are a consequence of occupational segregation by sex. Furthermore, occupational segregation often involves worse working
conditions in female-dominated occupations.
2. The tendency of women to concentrate in lowpay/status jobs has a negative impact on how
men see women and also on how women see
themselves. This reinforces gender stereotypes and fosters female poverty, which has
important consequences for female-headed
households (see ▶ Gender and Poverty).
3. Occupational segregation also favors gender
devaluation (according to which the sex of an
individual is an important factor in her/his
work’s worth) because it puts women in
weaker positions for resisting devaluation.
4. In addition, it has a negative effect on the
education of future generations, particularly
regarding the fields of study that boys and
girls opt to enter.
5. Following another line of reasoning, the
exclusion of women from certain occupations
implies a waste of human resources, which
leads to extremely inefficient results when
women are highly skilled people.
6. Moreover, sex segregation imposes important
rigidities, reducing the ability of the market to
respond to labor changes, which is a problem
in a global economy concerned with efficiency
and competitiveness.

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Cross-References
▶ Gender and Poverty
▶ Gender Discrimination
▶ Gender Inequalities
▶ Gendered Work
▶ Human Capital
▶ Negative Stereotypes
▶ Segregation

References
Akerlof, G., & Kranton, R. (2000). Economics and identity. Quarterly Journal of Economics, CXV(3),
715–753.
Albelda, R. (1986). Occupational segregation by race and
gender, 1958–1981. Industrial & Labor Relations
Review, 39(3), 404–411.
Altonji, J. G., & Blank, R. M. (1999). Race and gender in
the labor market. In O. Ashenfelter & D. Card (Eds.),
Handbook of labor economics, 3C (pp. 3143–3259).
Amsterdam: Elsevier.
Anker, R. (1998). Gender and jobs. Geneva, Switzerland:
International Labour Office.
Anker, R., Malkas, H., Korten, A. (2003). Gender-based
occupational segregation in the 1990’s. ILO working
paper 16/2003.
Baron, J. N. (1994). Organizational evidence of ascription
in labor markets. In P. Burstein (Ed.), Equal employment opportunity. Labor market discrimination and
public policy. New York: Walter de Gruyter.
Blau, F. D., Simpson, P., & Anderson, D. (1998). Continuing progress? Trends in occupational segregation
in the United States over the 1970s and 1980s. Feminist Economics, 4(3), 29–71.
Burnell, B. (1999). Occupational segregation. In J. Peterson & M. Lewis (Eds.), The Elgar companion to feminist economics. Cheltenham, UK: Edward Elgar
Publishing.
Cohen, P. N., & Huffman, M. L. (2003). Occupational
segregation and the devaluation of women’s work
across U.S. labor markets. Social Forces, 81(3),
881–908.
Del Rı́o, C., & Alonso-Villar, O. (2010). Gender segregation in the Spanish labor market: An alternative
approach. Social Indicators Research, 98(2), 337–362.
Duncan, O. D., & Duncan, B. (1955). A methodological
analysis of segregation indexes. American Sociological Review, 20(2), 210–217.
European Commission, Employment, Social Affairs and
Inclusion. (2009). Gender segregation in the labour
market:
Root
causes,
implications
and
policy responses in the EU. Accessed October 15,
2009,
from
http://ec.europa.eu/social/main.
jspcatId¼738&langId¼en&pubId¼364&type¼2&furtherPubs¼ yes.

Occupational Stress in a Multicultural Workplace
Fl€
uckiger, Y., & Silber, J. (1999). The measurement of
segregation in the labor force. Heidelberg, Germany:
Physica-Verlag.
Goldin, C. (2002). A pollution theory of discrimination:
Male and female differences in occupations and earnings. Working paper 8985, NBER.
Kaufman, R. L. (2010). Race, gender, and the labor
market: Inequalities at work. Boulder, CO: Lynne
Rienner Publishers.
King, M. (1992). Occupational segregation by race and
sex, 1940–88. Monthly Labor Review, 115, 30–37.
Massey, D. S., & Denton, N. A. (1988). The dimensions of
residential segregation. Social Forces, 67(2), 281–315.
Moir, H., & Selby Smith, J. (1979). Industrial segregation
in the Australian labour market. Journal of Industrial
Relations, 21, 281–291.
Petrongolo, B. (2004). Gender segregation in employment
contracts. Journal of the European Economic
Association, 2(2/3), 331–345.
Reardon, S. F., & O’Sullivan, D. (2004). Measures of
spatial segregation. Sociological Methodology, 34,
121–162.
Reskin, B. (1999). Occupational segregation by race and
ethnicity among women workers. In I. Browne (Ed.),
Latinas and African American women at work: Race,
gender, and economic inequality (pp. 183–204).
New York: Russell Sage.

Occupational Stress in
a Multicultural Workplace
Romana Pasca
Health Sciences, University of Northern British
Columbia, Prince George, BC, Canada

Synonyms
Demand-control-social support model of work
stress; Diverse cultural differences effects at
work; Effort-reward imbalance model of work
stress; Person-environment fit model of work
stress; Pressure/tension due to work; Social ecological model of work stress; Workplace culture
effects

Definition
▶ Stress is defined as the natural adaptive
response of the human body to external stimuli,

Occupational Stress in a Multicultural Workplace

such as events, demands, challenges, or physical
stressors that can place special demands on
the individual’s ability to positively cope with
the situation. Stress can be positive and negative
according to its outcome on health and wellbeing (Lazarus, 1999).
Positive stress, or eustress, is the good stress
that occurs as a result of situations that challenge
the individual’s abilities in a positive way (i.e.,
challenging an exam or taking up an exciting
project at work). The individual is able to
appraise the stressor and, therefore, engage their
entire set of coping abilities in successfully
dealing with the situation. Eustress is positively correlated to satisfaction, ▶ health, and
well-being.
Negative stress, or ▶ distress, develops as
a result of prolonged pressure and inability to
cope with the stressors or return to a relaxed
state even in the absence of the stressor.
Occupational stress refers to pressures
resulting from work activities.
Multicultural workplace brings together
a variety of individuals having diverse ethnic,
religious, and cultural backgrounds.

Description
Occupational stress is defined as tension and
strain resulting from work. The topic of occupational stress has been well researched and has
stimulated interest and controversy among occupational theorists. As a result, many theoretical
frameworks were developed in the attempt to
explain the factors that influence and trigger
the development of ▶ stress, the ways individuals may perceive stress, and the coping
mechanisms employed when dealing with stress
(Ettner & Grzywacz, 2001). There has been
strong consensus demonstrating that stress generated by work activities has the potential to
negatively affect the health and well-being of
individuals’ participating in the work process
(Baba, Jamal, & Tourigny, 1998; Elovainio,
Kivim€aki, Steen, & Kalliom€aki-Levanto, 2000).
However, increasing workplace diversity and
▶ multiculturalism bring into discussion the

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relevance of the existing theoretical models of
stress and their applicability to the current
workforce.
Multicultural workplaces represent a new
reality defining businesses and organizations.
Due to globalization trends and increased migration of the workforce across borders, businesses
and workplaces have started to recognize the
importance of adjusting and accommodating to
the needs of a diverse workforce. As such, modern and inclusive workplaces have the responsibility to contemplate multicultural approaches
that take into consideration workers’ individual
characteristics as well as their interests, needs,
and ethnic and cultural background.
Discussion
The perception of occupational stress is
influenced not only by work characteristics and
workplace environment but also by the individual
differences, familial, cultural, and social
context (Baba, Jamal, & Tourigny, 1998;
Amason et al., 1999). Every individual experiences stress in a distinctive and unique way.
Personality, along with components of social
environments, family relationships, and ethnic,
religious, and cultural backgrounds, has a strong
influence in shaping the way each individual
responds to and interprets events in the workplace. However, certain individuals are better
equipped to appraise and respond positively to
stressors at work, while others lack these
resources and abilities to deal with the work pressure and demands.
Stressors Emerging from Multicultural
Workplaces
The most common sources of occupational stress
are unanimously considered to be: workload
and work pace, conflict and ambiguity in the
workplace, career concerns, work scheduling,
interpersonal relationships, and job content and
control (Sauter, Murphy, & Hurrell, 1990). Most
of the research has analyzed occupational stress
on the population, looked at as a homogenous
majority. However, the relationships between
ethnic minorities and job stressors/health
outcomes have been pointing to additional

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stressors. These factors relate to discrimination
in the workplace, ▶ social interactions,
reestablishing professional status, and family
characteristics.

Occupational Stress in a Multicultural Workplace

way individual’s ▶ health and ▶ well-being is
affected. However, developing ▶ social support
for immigrant workers can be a daunting task
when considering the barriers and challenges
they have to face in the workplace.

Discrimination in the Workplace

A multicultural workforce consists of employees
with a wide range of backgrounds. On the positive
side, diversity can bring about a broader range of
perspectives on issues or challenges. People from
different backgrounds bring their own unique
cultural experiences to the situations they face in
their workplaces. However, adverse workforce
presents increased potential for discrimination.
Immigrants and visible minorities are often
exposed to discriminatory attitudes in the
workplace, being subject to stereotyping and
▶ prejudice (Pasca & Wagner, 2011; Xie, 1996).
These negative attitudes can trigger feelings of
low ▶ self-esteem, fear, ▶ anxiety, inability to
cope, and psychological distress.
Lack of Social Support and Social Interactions

From a workplace perspective, there is considerable evidence demonstrating that ▶ social
support at work represents an important ▶ mediator in mitigating the relationship between health
and occupational stress. Supportive and caring
organizations, involving management and supervisory structures in assuring good intraorganizational communication and support, are
more likely to have satisfied employees and
reduced uncertainty, dissatisfaction with job,
and occupational strain. However, diverse
individuals having diverse cultural and ethnic
backgrounds may perceive social support in the
workplace differently. In order to better understand the need of support in a multicultural workplace, training in cultural awareness and
communication skills plays a very important
role (Pasca & Wagner, 2011).
The quality of social interactions within the
multicultural workplace is another important
factor that has the potential to act as a buffer
in attenuating stress in the workplace. For immigrant workers, this aspect is paramount. Being
recognized, appreciated, and liked by coworkers
and supervisors play an important role in the

Reestablishing Professional Status

One of the most important barriers to adjusting
and settling into a new country consists in finding
employment in the field of expertise. Obtaining
a position at a commensurate level with their
education and acquired skills set is most of the
time impossible. While previous credentials
obtained in the country of origin are not recognized for employment purposes, the opportunities
to obtain work experience in the field of expertise
in the host country are very limited. This situation
puts immigrant workers at greater risk for
occupational stress and related health effects.
Family Characteristics

Immigrants are exposed to additional stressors
regarding family, social, and relationship issues.
The challenges faced by immigrants in the
complex process of integration and adaptation
to the new country may debilitate the family
dynamics and pose huge strain on family cohesion (Yost & Lucas, 2002). As such, in addition to
considering how workplace conditions affect
their health, there is need to examine the family
and social interactions beyond the workplace.
In an attempt to explain the mechanisms and
interrelationships behind these various responses
and interpretations to work-related stress,
occupational theorists have developed several
theoretical models. The most researched theories
of occupational stress are demand-control model,
effort-reward imbalance model, social ecological
model, and person-environment fit model.
Demand-Control-Social Support Model

One of the most influential theories of job stress
was developed by Karasek (1979) that proposes
a model which postulates that control over
one’s job acts as a protective factor in mediating
the relationship between work and health outcome (Karasek, 1979). In other words, high control within workplace provides the individual

Occupational Stress in a Multicultural Workplace

with autonomy, ability to make decisions, and
fulfill job tasks in a more creative way.
Conversely, low control in combination with
high demands in the workplace can result in
high job strain and negative health outcomes.
However, social support at work and in private
life has been proven to have a positive role in
mitigating the influence of stress on health. As
such, the model was expanded by considering
social support into the mix. The new model
suggested that low control and high demands, in
combination with low social support, had
a deleterious effect on the health of the individual. Although the main concept behind this new
framework emphasizes the important role played
by the social and interpersonal relationships
among the participants in the work process,
Karasek’s theory has its limitations. The model
was developed taking into consideration subjects
and issues specific to developed, highly industrialized countries. As such, the model does not take
into consideration the context of a multicultural
workplace. As such, the social aspect and
personal interactions at work, which are assumed
to be paramount for maintenance of health and
well-being and have significant implications in
the way individuals perceive work experiences,
are not analyzed from a multicultural perspective.
For example, immigrant population is considered
to be more vulnerable to high levels of stress due
to a series of circumstances relating to work,
social, and familial issues than their native counterparts. Empirical research has demonstrated
that these individuals tend to be more socially
isolated due to lack of connections within the
mainstream community, marginal socioeconomic status, lack of language proficiency, and
lack of knowledge of workplace culture and
▶ norms in the host country.
Effort-Reward Imbalance Model

This model addresses the imbalance that may
exist between the efforts and energies spent
fulfilling work demands and the reward or appreciation following these efforts in the workplace.
High efforts and poor rewards are associated with
high levels of dissatisfaction with one’s job and
high levels of occupational stress. Conversely,

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high efforts recompensed with high rewards or
recognition are more likely to stimulate positive
feelings, improve morale and ▶ self-esteem, as
well as lower the occupational stress and contribute to well-being and satisfaction. When looking
at this model from a multicultural perspective,
some issues need to be pointed out. For example,
in their process of adjusting to the new host
country, immigrants are often seen as lacking
appropriate skills, being unqualified or inadequately trained for the workplace, and lacking in
workplace initiative. As such, their efforts to
actively participate in the workplace are rather
shy, misunderstood, unappreciated, or not
rewarded by their coworkers or supervisors. The
additional stressors immigrants are exposed to by
simply trying to adapt and fit in the workplace, as
well as learning the technical language of the
workplace, the norms, and workplace culture in
order to successfully fulfill the requirements of
the job, are not captured in the model. Therefore,
additional research is required in order to
determine the effort-reward imbalance theoretical framework applicability to multicultural
workplaces.
Social Ecology Theory

This model focuses on both the person and the
environment. Stokols (1992) hypothesized that
individual characteristics, abilities, and attitudes
have a great deal of influence on how work
experiences are perceived, as well as on what
responses are associated with the work stressor.
In other words, people have the ability to appraise
the stressors in their environment and assess their
ability to manage and respond to the challenge.
As such, workers reporting a high level of
perceived constraints and neuroticism, pessimist
or hostile individuals, and working unusual shifts
or overtime will be more likely to report higher
level of occupational stress and ill-health. From
the perspective of the multicultural workplaces,
hosting a diverse workforce, the issue of evaluating work-related stressors becomes multifaceted
by having to take into consideration not only the
dynamic interplay among workers and
coworkers, groups and teams, and their individual characteristics but also their socio-physical

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environments, values, and cultural and ethnic
backgrounds. For example, group-oriented cultures, such as Asian, Eastern European, or Middle
Eastern countries, are seen as very dedicated to
their work and work groups, very loyal and bound
by strict rules of intra-group relationships. They
value work ethic and hierarchy, professional status, and social prestige. As such, a lack of understanding of the individual characteristics, values,
and cultural background and work environment
will create strain and negatively impact the individual’s health and well-being. One question that
may arise is whether the outcome measured following this theoretical framework is capturing
the true health effects of a particular job or rather,
simply capturing individual workers’ perceptions
at that particular time (e.g., the fact that immigrants are more likely to see themselves as
unadjusted/unable to cope with workplace/society demands, even when they are not).
▶ Person-Environment Fit

This model was developed by Caplan (1983) and
refers to the relationship existent between the
person and the work environment. In the context
of the new global economic trend, which influences the nature of work and work arrangements
and its impact on individuals’ well-being, the
concept of person environment (PE) fit has continued to be widely analyzed within the organizational and vocational behavior literature
(Holland, 1985; Caplan & van Harrison, 1993;
Tak, 2011). The term “fit” refers to the interaction
existent between the work personality of an individual and the work environment. If the interaction between the person and the environment is
congruent, the outcomes of this interaction, that
is, job satisfaction, commitment, and well-being,
are positive. In the case of incompatibility
between the two, the outcomes of the interaction
will be negative, translating in job dissatisfaction,
low commitment, and high level of stress. However, the current PE fit research considers this
correspondence between the characteristics of
the person and the work environment as having
a dynamic and reciprocal nature: in order to
obtain fit, the individuals have to actively respond
and adapt to the changing requirements of the

Occupational Stress in a Multicultural Workplace

work environment, while the components of the
work environment have to adjust and transform in
order to provide the reward and satisfaction for
the individual needs (Tak, 2011; Yu, 2009). Individuals having diverse cultural and ethnic
backgrounds may appraise differently the correspondence between their skills and abilities and
the work environment. Immigrant workers, in
their attempt to adjust and adapt to the host
country and to regain their professional status
lost through the relocation process in the new
country, may experience feelings of uselessness,
frustration, low ▶ self-efficacy and self-esteem.
These workers may consider as very important to
work towards regaining their professional status;
however, despite their desire to work in their
field of expertise, they will take on any type of
entry level jobs that can provide them with financial independence and help build their work
experience. This constrained misfit may have
deleterious effects on health, well-being, and
overall satisfaction. Therefore, the PE fit model
may be limited in properly capturing these relevant aspects contributing to the experience of
occupational stress.
Conclusion

Occupational stress in the multicultural workplace represents a pressing issue facing modern
organizations. Increased diversity in the workplace highlights the fact that a multicultural
approach to workplace stressors and related
health outcomes is required. Raising awareness regarding the specific characteristics
a multicultural workplace environment might
entail can play an important role in alleviating
ill-health and increasing the level of well-being
of the participants in the work process. The theoretical models of stress discussed so far
presented limitations when assessed against the
new reality of the multicultural workplaces.

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The American Psychologist, 45, 1146–1158.
Stokols, D. (1992). Establishing and maintaining healthy
environments: Toward a social ecology of health
promotion. American Psychologist, 47(1), 6–22.
doi:10.1037/0003-066X.47.1.6
Tak, J. (2011). Relationships between various person –
environment fit types and employee withdrawal
behavior: A longitudinal study. Journal of Vocational
Behavior, 78, 315–320.
Xie, J. L. (1996). Karasek’s model in the People’s Republic of China: Effects of job demands, control, and
individual differences. The Academy of Management
Journal, 39(6), 1594–1618.
Yost, A. D., & Lucas, M. S. (2002). Adjustment issues
affecting employment for immigrants from the former
soviet union. Journal of Employment Counseling, 39,
153–170.
Yu, K. Y. T. (2009). Affective influences in person –
environment fit theory: Exploring the role of affect as
both cause and outcome of P – E fit. The Journal of
Applied Psychology, 94(5), 1210–1226.

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Occupational Stress, Health,
and Well-Being
▶ Psychological Stress and Employee
Engagement

Occupational Well-Being
▶ Occupation Influence on Satisfaction/
Happiness

Occupational Well-Being and
Motivation of Those in the Helping
Professions
Philip D. Parker1, Andrew J. Martin2 and Theresa
Dicke3
1
Centre for Positive Psychology and Education,
University of Western Sydney, Penrith,
NSW, Australia
2
Faculty of Education and Social Work,
University of Sydney, Sydney, NSW, Australia
3
Department of Instructional Psychology,
Faculty of Educational Sciences University of
Duisburg-Essen Weststadtt€urme, Essen,
Germany

Synonyms
Motivation and Engagement Wheel (MEW)

Definition
Occupational well-being consists of two constructs on a continuum with ▶ burnout at one
end and engagement at the other (Maslach,
Schaufeli, & Leiter, 2001). It is of particular
relevance to research on helping professions
such as teaching, nursing, social workers and
the clergy (Schaufeli & Enzmann, 1998).

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Occupational Well-Being and Motivation of Those in the Helping Professions

Description
This continuum perspective of occupational
▶ well-being emerged from recent moves
toward ▶ positive psychology in the workplace
(see Martin, 2004a, 2004b) and a dual focus
within applied psychology on both workplace
▶ distress and engagement (e.g., Schaufeli,
Taris, & Rhenen, 2008). On one end of this
continuum is ▶ burnout, defined as a prolonged
and occupational specific form of strain
consisting of emotional exhaustion, depersonalization focused on recipients under the individual’s care, and feelings of a lack of personal
accomplishment (Byrne, 1991). Engagement is
also considered as a multidimensional construct
where the components are defined in an opposite
way to burnout, namely, vigor or high levels of
energy related to work, dedication and a sense of
significance of the work being conducted, and
absorption and ▶ happiness in one’s work
(Schaufeli, Salanova, González-Romá, &
Bakker, 2002). This continuum perspective has
now received extensive empirical validation
(Schaufeli et al., 2002, 2008) and is becoming
one of the most referenced theoretical models of
strain and well-being in the literature, particularly
in relation to the helping professions such as
teaching, nursing, the clergy, and social work
(Schaufeli & Enzmann, 1998).
The Helping Professions: The helping professions are a subset of the service or direct personrelated professions that are categorized by
emotional labor in ongoing interactions with clients (e.g., retail staff, flight attendants, teachers,
and nurses; see Dollard, Dormann, Boyd,
Winefield, & Winefield, 2003; Zapf, 2002). Helping professions or human service professions
share a number of features that may explain
why employees in the helping professions report
some of the highest levels of ▶ stress and burnout
of any professions (Schaufeli & Enzmann, 1998;
Smith, Brice, Collins, Matthews, & McNamara,
2000). Hasenfeld (1985) defines helping professions distinctly from other professions in that the
products of the job involve the maintenance, modification, and/or enhancement behaviorally, physically, or psychologically of recipients (or clients;

see Zapf, 2002) under their care. The workplace
roles of helping professions also differ from other
service professions where interactions with recipients (e.g., students, patients, and clients) are often
extended and can include exchanges that are highly
stressful, emotional, and potentially verbally and/or
physically dangerous. Compounding these difficulties is the fact that successful outcomes in the helping profession depend on the quality of
relationships between the recipient and the
employee (Dollard et al., 2003). One common feature of research on the helping professions is the
focus on burnout and workplace engagement
(Schaufeli & Enzmann, 1998). Burnout was first
researched and once thought solely the domain of
the helping professions (Byrne, 1991). Even now,
research on burnout is overwhelmingly focused on
nurses, teachers, social workers, and clergy/missionaries (Schaufeli & Enzmann, 1998). Recent
research on burnout, engagement, and retention
has highlighted self-beliefs, such as self-concepts,
as critical predictors of these outcomes particularly
in the helping professions (Cowin, Johnson, Craven, & Marsh, 2008; Friedman, 2000; Friedman &
Farber, 1992; Parker, Martin, Colmar, Liem, 2012;
Skaalvik & Skaalvik, 2007). In particular, positive
self-perceptions and adaptive motivation are seen
as protective – against burnout – and enabling, of
workplace engagement (Parker & Martin, 2009;
Parker et al., 2012).
Motivation, Self-Beliefs, and Occupational
Well-Being in the Helping Professions: Early
research on workplace well-being focused primarily on the role of ▶ motivation (e.g., Vogel,
Baker, & Lazarus, 1958; Vogel, Raymond, &
Lazarus, 1959). This focus on motivation has
declined over time, possibly because the literature appeared disjointed, theory-specific, and difficult to integrate into applied settings (Martin,
2007). As a result, much motivational research
remained theory-centric and did not include
a range of key motivational constructs that were
likely to be relevant to occupational well-being.
Working in other applied settings, Martin (2008)
drew together key motivational constructs providing a robust and differentiated exploration of
motivation that has been seen as critical to
occupational well-being. The Motivation and

Occupational Well-Being and Motivation of Those in the Helping Professions

Engagement Wheel (MEW) brings together
diverse aspects of motivational theorizing, providing a use-inspired, multidimensional approach
to motivation (Martin, 2007). The model harnesses need achievement, ▶ self-worth, goal
attribution, expectancy-value, and ▶ self-efficacy theories (Martin, 2007) and emphasizes
eleven
motivation
constructs
typically
represented by four higher-order factors: adaptive cognition (mastery, self-efficacy, valuing),
adaptive behavior (planning, persistence, task
management), impeding cognition (failure avoidance, ▶ anxiety, uncertain control), and maladaptive
behavior
(disengagement,
selfhandicapping). Research has indicated important
yields for understanding occupational well-being
in the helping professions using all (Parker &
Martin, 2011), or a subset of the motivation factors (Parker & Martin, 2009; Parker et al., 2012).
Self-beliefs are also critical elements in understanding the occupational well-being in the helping professions. In particular, Friedman (2000)
notes that burnout develops when helping
professionals’ initial positive perceptions of self
are confronted by the realities and difficulties of
the workplace. Similarly, Parker et al. (2012; see
also Parker & Martin, 2011), utilizing self-worth
theory as a basis, suggested that the occupational
well-being of helping professionals is partially
a function of the self-belief enhancing or
lowering messages they receive from the workplace. Self-worth motivation theory suggests that
a primary motivating drive in individuals is the
need to maintain and promote self-worth
(Covington & Beery, 1976). It further suggests
that ▶ achievement domains such as work
provide a basis upon which self-worth is evaluated and maintained, enhanced, or threatened via
its association with achievement-relevant personal attributes (Covington & Beery, 1976).
From a person-orientated perspective, individuals with positive occupational well-being are
most likely to be those who are successorientated (Parker & Martin, 2011). This is
because such individuals are characterized by
an incremental theory of personal attributes
where they see success as dependent on factors
under their control and obstacles as indicative of

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a need to apply more effort (Covington, 1992,
1998, 2000). Those who are at most risk of burnout are those who perceive their personal attributes as the sole determinate of their self-worth,
and thus the workplace is seen as a constantly
and personally evaluative (Covington & Beery,
1976). This can be expressed in the form of either
over-striving (persistent doubts about competence leading to extensive and disproportional
effort) or self-protection (efforts are focused on
protecting sense of self-worth often at the cost of
doing the job effectively). Surprisingly, failure
acceptors, who no longer aim to derive a sense
of self-worth from their occupation, experience
relatively little burnout (see Parker & Martin,
2011 for a review).
Discussion
Occupational well-being in the helping professions represents a positive psychology approach
to human functioning. Namely, the focus of
research and practice is to not only reduce incidents of workplace burnout but also promote
engagement and ▶ job satisfaction. Typical
research and intervention in this area has focused
on the characteristics of the helping profession
workplace (i.e., the environment); however,
research is beginning to note the importance of
an individual difference perspective such as
the focus on multidimensional motivation and
self-belief (see Parker et al., 2012).

References
Byrne, B. (1991). Burnout: Investigating the impact of
background variables for elementary, intermediate,
secondary, and university educators. Teaching and
Teacher Education, 7, 197–209. doi:10.1016/0742051X(91)90027-M.
Covington, M. V. (1992). Making the grade: A self-worth
perspective on motivation and school reform.
New York: Cambridge University Press.
Covington, M. V. (1998). The will to learn: A guide for
motivating young people. New York: Cambridge
University Press.
Covington, M. V. (2000). Goal theory, motivation, and
school achievement: An integrative review. Annual
Review of Psychology, 51, 171–200.
Covington, M. V., & Beery, R. (1976). Self-worth and
school learning. Fort Worth: Harcourt School.

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Hasenfeld, Y. (1985). The administration of human services. The ANNALS of the American Academy of Political Science, 479, 67–81.
Dollard, M. F., Dormann, C., Boyd, C. M., Winefield,
H. R., & Winefield, a. H. (2003). Unique aspects of
stress in human service work. Australian Psychologist,
38, 84–91. doi:10.1080/00050060310001707087.
Friedman, I. A. (2000). Burnout in teachers: Shattered
dreams of impeccable professional performance.
Journal of Clinical Psychology, 56, 595–606.
Friedman, I. A., & Farber, B. A. (1992). Professional selfconcept as a predictor of teacher burnout. The Journal
of Educational Research, 86, 28–35.
Cowin, L. S., Johnson, M., Craven, R. G., & Marsh, H. W.
(2008). Causal modeling of self-concept, job satisfaction, and retention of nurses. International Journal of
Nursing Studies, 45, 1449–1459. doi:10.1016/j.
ijnurstu.2007.10.009.
Martin, A. J. (2004a). Perplexity and passion: Further
consideration of the role of positive psychology in
the workplace. Journal of Organizational Behavior
Management, 24, 203–205.
Martin, A. J. (2004b). The role of positive psychology in
enhancing satisfaction, motivation, and productivity in
the workplace. Journal of Organizational Behavior
Management, 24, 113–133.
Martin, A. J. (2007). Examining a multidimensional
model of student motivation and engagement using
a construct validation approach. British Journal of
Educational Psychology, 77, 413–440.
Martin, A. J. (2008). Motivation and engagement in
diverse performance settings: Testing their generality
across school, university/college, work, sport, music,
and daily life. Journal of Research in Personality, 42,
1607–1612.
Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job
burnout. Annual Review of Psychology, 52, 397–422.
Parker, P., & Martin, A. J. (2009). Coping and buoyancy in
the workplace: Understanding their effects on
teachers’ work-related well-being and engagement.
Teaching and Teacher Education, 25, 68–75.
Parker, P., & Martin, A. (2011). Clergy motivation and
occupational well-being: Exploring a quadripolar
model and its role in predicting burnout and engagement. Journal of Religion and Health, 50, 656–674.
Parker, P. D., Martin, A. J., Colmar, S., & Liem, G. A.
(2012). Teachers’ workplace well-being: Exploring
a process model of goal orientation, coping behavior,
engagement, and burnout. Teaching and Teacher
Education, 28, 503–513.
Schaufeli, W. B., & Enzmann, D. (1998). The burnout
companion to study and practice. London: Taylor
and Francis.
Schaufeli, W. B., Salanova, M., González-Romá, V., &
Bakker, A. (2002). The measurement of engagement
and burnout: A two sample confirmatory factor analytic
approach. Journal of Happiness Studies, 3, 71–92.
Schaufeli, W. B., Taris, T., & van Rhenen, W. (2008).
Workaholism, burnout, and work engagement: Three

Occupational: Related to Work or Profession
of a kind or three different kinds of employee
well-being? Applied Psychology, 57, 173–203.
Skaalvik, E. M., & Skaalvik, S. (2007). Dimensions of
teacher self-efficacy and relations with strain factors,
perceived collective teacher efficacy, and teacher burnout. Journal of Educational Psychology, 99, 611–625.
Smith, A., Brice, C., Collins, A., Matthews, V., & McNamara, R. (2000). The scale of occupational stress:
A further analysis of the impact of demographic factors and type of job (Contact Research Report 311/
2000). London: Health and Safety Executive books.
Vogel, W., Baker, R., & Lazarus, R. (1958). The role of
motivation in psychological stress. Journal of Abnormal Psychology, 56, 105–112.
Vogel, W., Raymond, S., & Lazarus, R. (1959). Intrinsic
motivation and psychological stress. Journal of Abnormal Psychology, 58, 225–233.
Zapf, D. (2002). Emotion work and psychological
well-being: A review of the literature and some conceptual considerations. Human Resources Management Review, 12, 237–268.

Occupational: Related to Work
or Profession
▶ Occupational Health

Occupation-Based Mental Health
Programs
▶ Day Centers

Odds Ratio
Marc A. Adams1 and Terry L. Conway2
1
Exercise and Wellness, School of Nutrition and
Health Promotion, Arizona State University,
Phoenix, AZ, USA
2
Department of Family and Preventive Medicine,
University of California, San Diego, San Diego,
CA, USA

Synonyms
Cross product ratio; Prevalence odds ratio;
Relative odds

Odds Ratio

4465

Definition
The odds ratio is a measure of the magnitude of an
association (statistical effect, nonindependence)
between two binary (or dichotomous) variables.
The odds ratio is a ratio of two odds: the odds of
event of interest occurring within the sample of
interest to the odds of the event of interest occurring within a reference sample. The odds ratio is
asymmetrical and can range from 0 to infinity;
the odds ratio cannot be negative. Odds ratios
between 0 and 0.99 indicate a lower risk, between
1 and infinity indicate a higher risk, and equal to
1 indicate no relationship between two variables.
The following equation can be used to calculate
the odds ratio from the table below:
OR ¼

ða=cÞ

=ðb=dÞ ¼

ðadÞ

=ðbcÞ



95 % CIOR ¼ exp ln OR  1:96SEðlnORÞ
where a represents the count of individuals with
the event of interest in the sample of interest,
b represents the count of individuals with the
event of interest in the reference sample,
c represents the count of individuals without the
event of interest in the sample of interest, and
d represents the count of individuals without the
event of interest in the reference sample.

Met exercise
guideline
Did not meet
Total

High
function
30

Low
function
5

Total
35

70
100

95
100

165
200

Description
Odds are a way to represent probabilities (r).
Odds can be calculated using either probabilities
or counts (frequencies) of an event (Bland &
Altman, 2000). The odds of an event is the ratio
of probability of the event of interest occurring to
the probability of the event not occurring
[odds ¼ r/(1r)].
The table presents data for a hypothetical
cross-sectional study of older adults with high

O

and low physical functioning and meeting physical activity guidelines or not. Suppose the probability of a group of 100 high-functioning older
adults meeting national physical activity recommendations was 30.0 % (30/100) or an odds
of 42.86 [odds ¼ .30/(1 .30)]. Further suppose
that the probability of 100 low functioning
adults meeting recommendation was 5.0 %
(odds ¼ 5.26).
The ratio of the two odds is useful if a relative
comparison between the groups is warranted, for
example, if we wanted to compare those with
high and low physical function on meeting physical activity recommendations. In this case, the
odds ratio would be 8.15 (42.86/5.26), and
the interpretation of this result would be
“the odds of meeting physical activity guidelines
among high-functioning older adults was
8.15 times the odds among low functioning
older adults.”
The odds ratio is unique in that we can flip the
question and ask, “For those who meet physical
activity guidelines, what is the odds of being in
the high compared to low functioning group?”
The odds ratio would again be 8.15.
When the relative risk is inappropriate to calculate, such as case–control study designs, the
odds ratio can be used to approximate relative
risk. Thus, the odds ratio can be used for either
retrospective or prospective study designs (Kline,
2004). For prospective studies, relative risk is
generally preferred to the odds ratio, but odds
ratios are commonly reported in these designs.
A caveat is that odds ratio does not approximate
relative risk when the event of interest is relatively common (e.g., incidence > 10 % in the
population) (Zhang & Yu, 1998).
Taking the natural logarithm of the odds
(i.e., log-odds or logit) has several advantages
for more sophisticated statistical modeling
(Rothman, Greenland, & Lash, 2008). The logit
has an approximately normal distribution that
ranges from negative to positive infinity. The
logit’s mathematical properties make it more
convenient for statistical modeling (e.g., logistic
regression) because a 1-unit change in a predictor
variable results in 1-unit change in a dependent
variable.

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Cross-References
▶ Effect Size
▶ Logistic Regression
▶ Log-Logistic Models

References
Bland, J., & Altman, D. (2000). Statistics notes: The odds
ratio. British Medical Journal, 320(7247), 1468.
Kline, R. (2004). Beyond significance testing: Reforming
data analysis methods in behavioral research.
Washington, DC: American Psychological Association.
Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern epidemiology. Philadelphia: Lippincott Williams &
Wilkins.
Zhang, J., & Yu, K. F. (1998). What’s the relative risk?
A method of correcting the odds ratio in cohort studies
of common outcomes. Journal of the American
Medical Association, 280(19), 1690–1691.

OECD List of Social Indicators
Monika Queisser1, Maxime Ladaique1 and
Marco Mira D’Ercole2
1
Organization for Economic Cooperation and
Development (OECD), Paris, France
2
Household Statistics and Progress
Measurement, Organisation for Economic
Co-operation and Development (OECD), Paris,
France

Definition
The Organisation for Economic Co-Operation
has been compiling sets of social indicators
since the 1970s and before. This article outlines
that history.

Description
In the 1960s–1970s. . .
Social indicators were developed at the OECD
even before member countries had established

OECD List of Social Indicators

comprehensive programs of their own. Calls for
developing system of “social accounting” began
in the 1960s–1970s, based on the view that, by
drawing on countries’ practices and approaches,
it would be possible to develop more comprehensive reporting of social conditions and to adopt
common statistical definitions making international comparisons possible. The OECD Social
Indicators Programme was thus launched in 1970
with the objective of generating a comprehensive
body of evidence on social conditions in member
countries. The first major achievement of the
program was the publication in 1973 of a “List
of Social Concerns Common to Most OECD
Countries.” Following this, several years of
developmental work ensued aimed at designing
indicators to measure these concerns.
In the 1980s. . .
OECD List of Social Indicators (1982)
The OECD List of Social Indicators was designed
to describe the main social characteristics that
were common to all OECD countries at that
time. The OECD report argued that the selection
of an international list of social indicators should
be viewed by member countries as providing
a general framework for measurement as well as
guidelines for building indicators in specific
fields. The report presented a set of indicators
designed to evaluate some fundamental aspects
of people’s social conditions in OECD countries.
For each indicator, the report provided
a specification, statistical guidelines, and the
disaggregation criteria necessary for determining
the situations of various population groups.
In terms of selection criteria, the report argued
that social indicators should be output-oriented,
relevant to policy, available over a long period of
time, relevant to describe individual well-being
independently of the specific institutional
arrangements of each country. Further, the
report argued that the indicators should be
reasonably comparable, comprehensive, closely
corresponding to major social concerns, and
providing an integrated framework. Overall,
33 indicators were included in the OECD list.
A number of member countries carried out

OECD List of Social Indicators

specific surveys based on the OECD Comprehensive Survey Questionnaire. After having reached
agreement on the list, the next phase of OECD
work was to compile national data sets for each
social concern, depending on the ability of
member countries to provide the relevant data.
Suggested disaggregations for the indicators
were age, gender, household type, etc. An inventory of data sources was established at the OECD
for each of its member countries.
Living Conditions in OECD Countries:
A Compendium of Social Indicators (1986)

The purpose of this compendium was to present
the information obtained from the indicators
included the 1982 OECD List, giving a general
idea of living conditions in OECD countries.
The compendium described similarities and
differences between OECD countries as revealed
by the data. Social indicators pertained to 15
areas, i.e., length of life, healthfulness of life,
use of educational facilities, learning, availability
of employment, quality of working life, use of
time, income, wealth, housing conditions, accessibility to services, environmental nuisances,
social attachment, exposure to risk, and perceived
threats. While the OECD continued to collect
indicators on the different dimensions of social
conditions in later years, no comprehensive
compilation of these indicators was released by
the OECD until the early 2000s.
Since 2000. . .
Society at a Glance: OECD Social Indicators
(2001–2013)

Since 2001, the OECD has published Society at
a Glance, a biennial overview of the social
situation in OECD countries. Society at
a Glance aims to address the growing demand
for quantitative evidence on social well-being
and its trends across OECD countries. It informs
responses to two questions: What progress have
countries made in their social development? and
How effective have been the actions of societies
in furthering social development? The first issue
of Society at a Glance in 2001 contained 45
indicators, which were reduced to 25 in the

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sixth edition in 2011. The indicators included in
Society at a Glance are organized based on
a variant of the “Pressure-State-Response”
framework used for environmental indicators,
which groups indicators into three areas: “social
context,” “social status,” and “societal response.”
The second dimension of the framework used in
Society at a Glance groups indicators according
to the broad policy fields that they cover, with
four broad objectives of social policy used to
classify indicators of social status and societal
response: “self-sufficiency,” “equity,” “health
status,” and “social cohesion.” Beginning in
2006, Society at a Glance has also included
special chapters on selected topics, most recently
on unpaid work. The next edition will be
published early 2014. More information and latest data are available at www.oecd.org/social/
indicators.
How’s Life? Measuring Well-Being (2011–2013)

Following the release of the report by the Commission on the Measurement of Economic
Performance and Social Progress (better known
as Stiglitz-Sen-Fitoussi Commission) in 2009,
the OECD started releasing a biannual report on
people’s well-being. This report, which is part of
the activities undertaken by the OECD Statistics
Committee on measuring performance “beyond
GDP,” includes a list of indicators pertaining to
human
well-being,
a
broad
concept
encompassing both “material living conditions”
and “quality of life”; these two domains are
further declined in terms of 11 dimensions
(“income and wealth,” “jobs and earnings,” and
“housing” with respect to material living conditions; “health status,” “work and life balance,”
“education and skills,” “social connections,”
“civic engagement and governance,” “environmental quality,” “personal security,” and “subjective well-being” with respect to quality of
life). How’s Life 2011includes 22 headline
indicators of human well-being, which are
meant to be used for benchmarking and monitoring purposes. Relative to the framework used for
Society at a Glance, the How’s Life? indicators
are limited to outcome measures (i.e., excluding

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indicators of policy responses) but include
a broader range of domains. The How’s Life?
framework encompasses measures of both average achievements and inequalities for all the
11 dimensions and includes “sustainability of
well-being over time” as a separate domain. The
Your Better Life Index (www.betterlifeindex.
org), developed by the OECD, is a Web application that enables users to build customized indices
of overall well-being based on their own
views about the importance of the different
dimensions of people’s life and on comparative
measures of countries’ average achievements
(normalized and aggregated within each domain)
in each of the 11 dimensions of How’s Life? The
next edition of How’s Life? will be published at
the end of 2013.

Cross-References
▶ Progress of Societies: OECD Projects
▶ Social Indicators
▶ Social Indicators Research

References
OECD. (1982). OECD list of social indicators. Paris:
OECD Publications and Information Center.
OECD. (1986). Living conditions in OECD countries –
a compendium of social indicators. Paris: OECD
Publications and Information Center distributor.
OECD. (2001). Society at a glance 2001 – OECD social
indicators. Paris.
OECD. (2002). Society at a glance 2002 - OECD social
indicators. Paris: OECD – Organisation for Economic
Co-operation and Development.
OECD. (2005). Society at a glance 2005 – OECD social
indicators. Paris.
OECD. (2006). Society at a glance 2006 – OECD social
indicators. Paris.
OECD. (2009). Society at a glance 2009 – OECD social
indicators. Paris.
OECD. (2011a). Society at a Glance 2011 – OECD Social
Indicators. Paris. www.oecd.org/social/indicators.
OECD. (2011b). How’s life? Measuring well-being. Paris.
www.oecd.org/howslife.
Stiglitz, J. E., A. Sen and J-P. Fitoussi (2009),
Mismeasuring Our Lives: Why GDP Doesn’t Add
Up, the report by the Commission on the Measurement
of Economic Performance and Social Progress, The
New Press.

OECD Main Economic Indicators

OECD Main Economic Indicators
David Brackfield
Organization for Economic Cooperation and
Development, Paris, France

Definition
The OECD’s Main Economic Indicators (MEI)
database provides – as of January 2013 – a picture
of the most recent short-term economic statistics
through almost 30,000 series covering the 34
OECD member countries, six major nonmember
economies (Brazil, China, India, Indonesia, the
Russian Federation, and South Africa), and
a selection of area aggregations and zones (e.g.,
OECD area, Euro area, European Union, Major
Seven). It provides, where available, monthly,
quarterly, and annual data for each country and
aggregate. The MEI is fundamentally a collection
of disparate subject areas whose common glue is
their timely nature.
MEI data are made available via OECD.Stat,
an online platform where users can search for and
extract data from across OECD’s many databases
including the MEI. OECD has gradually started
to release data subsets updated on a daily basis.
These updates reflect information collected from
various sources, such as National Statistical
Offices.

Description
MEI Database and Printed Publication
MEI data are traditionally made available on
a monthly basis in print, PDF, CD-ROM, CSV,
and as interactive database online via OECD’s
iLibrary. This monthly release process follows
a regular calendar, published a year in advance.
In recent years, changes have been made to the
structure and content of the Main Economic
Indicators publication. In summary, these reflect
an increased emphasis on cross-country
comparability with a greater use of composite
indicators, notably more derived series

OECD Main Economic Indicators

(e.g., activities as a share of GDP), and more
series being presented in growth rates. There are
a number of factors driving these changes. Key
among them is the desire for the database to retain
its relevance as a key source of comparable
short-term economic statistics and to ensure that
the content remains as accessible as possible,
from a user perspective. Other factors play
a role, however.
When the MEI started many years ago, the
range of short-term indicators for OECD member
countries was a lot more diverse and sparse than it
is today, necessitating the development of
a publication and database that acted as
a medium (a storage facility as such) to provide
users with these diverse indicators. Over time,
however, as countries have increased and
harmonized their range of short-term indicators,
cross-country diversity has diminished, and most
of the key variables within the MEI database are
now available for most countries. Given that the
primary competitive advantage of the MEI is in
the provision of cross-country comparable data, it
is here that the database will continue to focus.
Publication Footnotes
For simplicity, only a limited number of types of
generic footnotes are used in the MEI paper
publication:
1. Coverage: This refers to statistics that align
with the target concept being measured but
whose coverage of activities, products, and
services only partially (but still significantly)
or more than covers the activities, products,
and services specified in the target concept.
For example, measures of industry including
construction may be used instead of estimates
of industry excluding construction.
2. Proxy: Closely related to the “coverage”
footnote will be the “proxy” footnote. This
refers to statistics whose underlying concept
does not align with the target concept; for
example, producer prices might be used as
a consumer prices. The footnote also refers to
cases where the secretariat makes its own
estimates, for example, by deriving estimates
of retail trade volume using current price
figures deflated by a consumer price index.

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3. Point in time: Some series are measured on the
basis of a specific point in time, for example, the
end of the month, or they are measured by taking
the average of daily observations over the
month, year, etc. However, it is not always possible to collect statistics that fully subscribe to
these conventions; for example, data may only
be available on say every Wednesday. These
types of divergences from the standard-targeted
measure are flagged-up under this heading.
4. Information item: This footnote mainly reflects
information items worthy of mention but that do
not significantly affect the usability of the data.
For example, the footnote might be used to refer
to the specific source information used in
collecting the statistics which might, from an
international perspective, differ from more traditional sources. For example, a household budget survey might be used instead of a labor force
survey to gain information on employment.
MEI Topics
The MEI brings together short-term economic
statistics from a number of different subject
areas: Labor Statistics, Unit Labor Costs
(ULCs), Composite Leading Indicators (CLIs),
International Trade and Balance of Payments,
Quarterly National Accounts, Production Indicators, and Financial Indicators and Prices. It is
important to note that there are some additional
factors that require consideration. Chief among
these is the role of the CLI and its relation to other
short-term indicators. Other subject areas in the
MEI family add value by allowing users to find
internationally comparable indicators in the same
place. In this sense, the indicators are not in and
of themselves market sensitive because they are
already in the public domain when they are
released by the OECD. The CLI is different,
however, in that it is genuinely new information
that relies on the timely updating of a number of
other subject areas.
At present, the following indicators have
associated press releases: harmonized unemployment rate, ULCs, consumer prices, and CLIs.
The Financial Statistics topic contains
predominantly monthly statistics and contains
financial statistics on five separate subjects:

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Monetary Aggregates, Interest Rates, Exchange
Rates, Reserve Assets, and Share Prices. Data are
available either as an index or as a level
depending on which measure is seen as the most
appropriate and/or useful in the economic
analysis context.
The Production and Sales topic contains
industrial statistics on four separate subjects:
Production, Sales, Orders, and Work started.
Most data are available monthly and are
presented as an index or as a level depending on
which measure is seen as the most appropriate
and/or useful in the economic analysis context.
Due to differences in statistical or economic
environment at country level, however, availability of data varies from one country to another.
The Balance of Payments is a statistical
statement that provides a systematic summary
of economic transactions of an economy with
the rest of the world, for a specific time period.
The transactions are for the most part between
residents and nonresidents of the economy.
A core group of approximately 30 components
are provided, including the main balances and
financial flows.
Business and Consumer Opinion (Tendency)
Surveys provide qualitative information that has
proved useful for monitoring the current
economic situation. Typically, they are based on
a sample of enterprises or households, and
respondents are asked about their assessments of
the current situation and expectations for the
immediate future. For enterprise surveys, this
concerns topics such as production, orders, and
stocks, and in the case of consumer surveys, their
intentions concern major purposes, economic
situation now compared with the recent past,
and expectations for the immediate future.
Many survey series provide advance warning
of turning points in aggregate economic activity
as measured by GDP or industrial production.
Virtually all business tendency and consumer
opinion survey data are presented as time
series of balances, either in raw or seasonally
adjusted form.
The monthly International Trade topic
contains international trade statistics for exports,
imports, trade balance, and world trade statistics.

OECD Main Economic Indicators

The Short-Term Labor Market Statistics topic
contains predominantly quarterly labor statistics
that compiled from sample household surveys on
a monthly or quarterly basis. It is widely accepted
that household surveys are the best source for
labor market key statistics. Subjects available
cover the following: working age population by
age; active and inactive labor force by age;
employment by economic activity, by working
time, and by status; and unemployment
(including monthly harmonized unemployment)
by age and by duration. Data is expressed
in levels (thousands of persons) or rates
(e.g., employment rate) where applicable.
The Registered Unemployment and Job
Vacancies topic contains predominantly monthly
statistics from administrative data which are
therefore subject to national legislation which
evolve through time. Consequently registered
unemployment data are not comparable across
countries. The number of registered unemployed
persons and registered unemployment rates are
presented because they are monthly and quickly
available after their reference period. Job vacancies
data provides estimates of the number of unfilled
job vacancies across national economies.
The Hourly Earnings topic provides monthly
and quarterly data on employees’ earnings series.
It includes earnings series in manufacturing and
for the private economic sector. Mostly, the
sources of the data are business surveys covering
different economic sectors, but in some cases,
administrative data are also used.
Unit Labor Costs measure the average cost of
labor per unit of output. They are calculated as
the ratio of total labor costs to real output. The
OECD System of Unit Labor Cost and Related
Indicators, which is part of the MEI database,
produces annual and quarterly unit labor cost
measures (productivity, labor compensation per
unit labor input) according to a specific
methodology to ensure data are comparable
across OECD member countries.
The Prices topic contains predominantly
monthly statistics covering consumer price and
producer price indices. The data available are
chosen as the most relevant prices statistics for
which comparable data across countries is

Offense Definitions’ Impact on Criminal Justice Data Quality

available. In all cases, effort has been made to
ensure that the data are internationally comparable across all countries presented and that all the
subjects have good historical time-series’ data to
aid with analysis.
The OECD Composite Leading Indicators
(CLI) topic includes CLIs calculated for 33
OECD countries (Iceland is not included), 6
nonmember economies, and 8 zone aggregates.
A country CLI comprises a set of component
series selected from a wide range of key shortterm economic indicators mainly covered in the
MEI database. CLIs, reference series data, and
standardized business and consumer confidence
indicators are presented in various forms.
The Quarterly National Accounts topic
provides comparable macroeconomic data for
countries on a quarterly basis. Quarterly national
accounts feed directly into OECD’s modelling,
forecasting and analytical work and are widely
used by outside researchers for the same purpose.
Subjects included in the MEI database include
GDP by type of expenditure and the GDP Price
Deflator.
The MEI Original Release Data and Revisions
database provides access to time series data for
21 key economic variables as originally
published in each monthly edition of the MEI
from February 1999 onwards. This real-time
database enables economists to perform realtime data analysis of econometric models and
statisticians to study the magnitude and direction
of subsequent revisions to published statistics.
Subjects included are the following: GDP, Total
and Expenditure Components; Index of Industrial
Production; Production in Construction; Composite Leading Indicators; Retail Trade Volume;
Consumer Price Index; Harmonized Unemployment Rates; Employment; Hourly Earnings in
Manufacturing; Monetary Aggregates – Broad
Money; International Trade in Goods; Balance
of Payments – Current Account Balance.

Cross-References
▶ Consumer Price Index
▶ Economic and Social Indicators

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▶ Economic Development
▶ Economic Growth
▶ Gross Hourly Earnings of Skilled Workers
▶ Gross National Product (GNP)
▶ Inflation, Public Concern with
▶ Unemployment

References
MEI Sources and Definitions – http://stats.oecd.org/mei/
default.asp?lang¼e
OECD Data Dissemination Tool, OECD.Stat Extracts –
http://stats.oecd.org/Index.aspx
OECD iLibrary, MEI Publication – http://www.oecdilibrary.org/
OECD Statistics Directorate, Home Page – http://www.
oecd.org/std/

OES
▶ Oxford Elbow Score

Offense Definitions’ Impact on
Criminal Justice Data Quality
Stefan Harrendorf
Institute of Criminal Law and Justice, GeorgAugust-Universit€at Göttingen, Göttingen,
Germany

Definition
Data recorded in official crime and criminal
justice statistics strongly depend on the legal
system. This makes interpretation of official
crime and criminal justice data a very complex
task, especially when it comes to international
comparisons. Offense definitions have an especially strong impact on the international comparability of criminal justice data. In national
statistics they will necessarily depend on offenses
defined in criminal law. Thus, comparing categories from national statistics bearing the same, yet

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Offense Definitions’ Impact on Criminal Justice Data Quality

translated, offense names is like comparing
apples with oranges disguised as apples: Theft is
not vol is not Diebstahl is not kradziez is not
hurto, and if you compare different legal systems
that all use the English language, theft is not even
always theft. This makes it necessary to base
any international comparisons on data from international criminal justice surveys. But it can be
shown that even if relying on these surveys, many
differences remain which make comparisons still
very problematic. International ▶ victimization
and self-reported delinquency surveys are therefore the best sources for international comparison
of actual crime levels.

Description
Obviously, crime-related issues (like ▶ victimization and ▶ fear of crime) influence ▶ quality of
life, even if these effects are in general rather
weak and should not be overestimated (Michalos
& Zumbo, 2000). The intensity of effects depends
on the type of crime in question (Hanson, Sawyer,
Begle & Hubel, 2010).
One way to ▶ estimate actual crime levels for
different offenses is by looking into the official
crime and criminal justice statistics. These statistics do not come close to the “true picture” of
crime in a country: Even police statistics are
mainly statistics on police work and workload
and do not mirror the “reality” of crime in
a given area (Harrendorf, 2011, pp. 126–128).
Problems even multiply if we want to compare
criminal justice data between countries.
International Comparability of Crime and
Criminal Justice Data
We always need to keep in mind that results
presented in crime and criminal justice statistics
are influenced by substantial, legal, and statistical
factors (Aebi, 2008; von Hofer, 2000):
1. Substantial factors: Recorded crimes are just
a small fraction of all crimes occurring in
a country, and there is no fixed relation
between the dark figure of crime and those
crimes that have been recorded by the police
(see ▶ Crime Estimates). The relation depends

on the number of victims or witnesses who,
after realizing that a crime happened, decide to
report it to the police. It also depends on the
efficiency of police work, especially for those
offenses which are victimless or for which
the victim is not interested in making a report.
2. Legal factors: The work of the police, the
prosecuting authority, and the criminal courts
is ruled by criminal law. Statistical categories
will therefore necessarily depend on the legal
definitions of the different crimes. They will
also depend on criminal procedure law and the
practical functioning of the criminal justice
system.
3. Statistical factors: The data recorded in
crime and criminal justice statistics are also
influenced by rules of statistical recording. For
example, in some countries police statistics
are input statistics, that is, recording a crime
the moment it comes to the attention of the
police. In other countries crimes are recorded
when the investigation is completed (output
statistics). It has been shown that the use of
input vs. output statistics can explain a great
deal of variance in criminal justice statistics
(Aebi, 2008).
Therefore, it is not possible to simply look into
the different national statistics and compare
categories which bear the same, yet translated,
name (Harrendorf, 2012). It is instead necessary
to rely on international survey data (cf. Lewis,
2012). International criminal justice surveys like
the United Nations Crime Trends Survey (UN
CTS) or the European Sourcebook of Crime and
Criminal Justice Statistics (ESB) try to foster
comparability as far as possible and make the
remaining differences visible. Ideally, they collect their data via a standardized questionnaire
sent out to a network of correspondents (i.e.,
experts for national crime and criminal justice
data) in the different responding countries,
followed by a thorough data validation process
(detailed on ESB methodology Harrendorf, 2012;
Jehle & Harrendorf, 2010).
But even ambitious endeavors cannot lead to
full comparability of crime and criminal justice
data across the world, since the influence of the
three factors discussed above cannot be fully

Offense Definitions’ Impact on Criminal Justice Data Quality

eliminated in secondary data analysis. According
to the fourth edition of the ESB (Aebi et al.,
2010), for example, the European rates per
100,000 population for theft in 2006 range from
87 in Albania to 6,323 in Sweden (mean: 2,008;
median: 1,697). Though these differences can be
explained at least partially with the metadata
collected in the survey, there is no obvious connection between the rates and the “reality” of
crime in a given country. Users of international
crime and criminal justice statistics are therefore
advised rather to compare trends over years than
rates per 100,000 population (Aebi et al., 2010, p.
24; Tavares, Thomas, & Bulut, 2012, p. 15), with
one possible exception for intentional homicide
(Geneva Declaration Secretariat, 2011; Malby,
2010; UNODC 2011; also see below).

Offense Definitions in International
Crime and Criminal Justice Surveys
The most important way to enhance comparability is by using a standard definition for each
offense, that is, a definition for this offense as it
is understood for the purpose of the survey. In the
following, the way standard definitions are used
in the ESB will be described. However, the data
collection undertaken at EuroStat follows the
ESB concept (Tavares et al., 2012, p. 15), and
the UN CTS also uses standard definitions. In the
ESB the standard definition is accompanied by
a list of items that should, if possible, be included
in the data and a list of items to be excluded.
These include and exclude lists are meant to
address cases of doubt for which it might not be
clear whether the described forms of behavior can
still be seen to fulfill the standard definition.
Instead of sticking to their own national concepts,
experts are required to provide data that are in
accordance with the standard definitions and to
follow all the include and exclude rules. If a rule
cannot be followed, correspondents will note this
in the questionnaire. How far each country was
able to follow the given rules is documented in
detail in the ESB (Aebi et al., 2010, pp. 341–375).
For example, for intentional homicide, the
ESB standard definition is intentional killing of

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a person, and correspondents were required to
include assault leading to death, euthanasia,
infanticide, and attempts, but to exclude assistance with suicide; for robbery, it is stealing
from a person with force or threat of force,
including muggings (bag-snatchings), theft
immediately followed by force or threat of force
used to keep hold of the stolen goods, and
attempts, but excluding pickpocketing, extortion,
and blackmailing. In total, the fourth edition
of the ESB featured 27 offense groups and
subgroups on police level and 21 on the other
levels of the criminal justice process, of which
18 had a separate standard definition. For a full
list of definitions, see Harrendorf (2012,
pp. 29–33), Aebi et al. (2010, pp. 347–375).

Quality of the Definitions Used and Data
Comparability Achieved
Users of international crime statistics must be
especially cautious when comparing total crime
rates: A term like “total criminal offenses
recorded by the police” is a black box with
respect to the offenses covered therein
(Harrendorf, 2011). This is due to the fact that
the borderline between criminal and noncriminal
behavior is drawn somewhat differently in each
country. And even for the offenses considered
criminal, it is not necessarily clear that all of
these are included in police statistics, especially
for petty offenses. Therefore, the total number of
offenses recorded by the police is (if put into
relation to the number of police officers
in a given country) perhaps a measure of its
workload and performance (see Harrendorf &
Smit, 2010), but it is not at all a measure of crime.
Theoretically, it can be expected that comparability is best for severe offenses with a very
simple and convergent definition. The most
prominent and perhaps only offense to fulfill
both prerequisites is intentional homicide.
Although differences remain, they do not seem
to affect the “core” of the offense, making homicide the offense that is by far the easiest to be
compared worldwide (see, inter alia, Geneva
Declaration Secretariat, 2011; Malby, 2010;

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Offense Definitions’ Impact on Criminal Justice Data Quality

UNODC 2011). Other severe offenses are
typically much less convergent in definition
(take, e.g., rape or robbery), while less severe
and especially petty offenses will not always be
handled fully inside criminal justice but
be diverted out of the system at the very beginning of the criminal justice process or at any time
later. The extent to which and the way this
happens differ significantly from country to
country (cf. Elsner, Smit, & Zila, 2008; Wade
et al., 2008) and do affect not only the quantity
of recorded offenses but also the quality (e.g., if
theft offenses under a certain minimum value are
not considered criminal, as in some Central and
Eastern European jurisdictions, like in Poland).
Harrendorf (2012) tested the quality of the
offense definitions used in the fourth edition
ESB (Aebi et al., 2010). In order to do so, he
used three different measures:
1. Overall conformity with the definitions, that
is, the rate (in %) of countries which were able
to follow a definition fully and provide data
for it. A definition was considered to have
been followed fully if a correspondent
included in the data all the items on the include
list and excluded all items on the exclude list.
This was done differentiated for the police and
convictions level.
2. Item conformity, that is, the rate (in %) of
countries that were able to follow a single
include or exclude rule among those countries
that provided data for the respective offense.
Mean and median item conformity rates were
also calculated for each definition. Example:
A mean item conformity rate of 82 % for
homicide on police level means that on average 82 % of all countries that provided data
were able to follow a single rule of the homicide definition.
3. Variation of data across countries: As already
explained, there is huge variation in the rates
per 100,000 population produced in any
endeavor to measure crime in international
comparison. Therefore, it is impossible to
assess the quality of a definition just by the
diversity of results produced. Harrendorf
(2012, p. 45f.) was now able to show that for
a given group of countries in a given year, the

relation between the standard deviation and
the mean of rates per 100,000 population for
different offense groups can be approximated
by a proportional function. Thus, the variation
coefficient (i.e., the standard deviation divided
by the mean) can be expected to be approximately constant across all offense groups.
If the variation coefficient for a certain offense
group differs significantly from the median
variation coefficient for all offense groups
together, this may therefore also indicate
a problem with the definition.
The most important results are summarized in
Table 1. For more detailed information, please
refer to Harrendorf (2012). Overall results show
that the best standardization could be achieved
for robbery, theft total, and drug offenses total.
These offenses show very good conformity rates
and a variation coefficient below or not too far
above the median (offenses, 1.090; suspects,
1.101; convictions, 1.073). While two of these
offenses are “traditional” ones, drug offenses
have been subject to several international, often
worldwide, harmonization initiatives (especially
via the UN drug conventions; see Harrendorf,
2012 with additional references).
The variation coefficients generally show
what could already be expected: There is very
strong variance in the data. But there are three
offenses that show a variation coefficient that is
clearly higher than that of the other offenses:
traffic offenses, computer offenses, and money
laundering.
For traffic offenses, it is known that in
some countries even minor traffic offenses
(like parking offenses) are (formally) considered
criminal. While the ESB intends to collect data
only on the more severe traffic offenses
(like driving under the influence), this could
obviously not be achieved until so far. More
striking are the results for computer offenses
and money laundering, both of which have
been subject to EU-wide harmonization
activities (see Harrendorf, 2012 with additional
references). However, it has got to be noted for
computer offenses that this is a very broad category (for the definition, see Harrendorf, 2012,
p. 31f.). The offenses included will not

Offense Definitions’ Impact on Criminal Justice Data Quality

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Offense Definitions’ Impact on Criminal Justice Data Quality, Table 1 Quality of offense definitions used in the
fourth Ed. ESB, based on three indicators (Aebi et al., 2010; Harrendorf, 2012)
Overall conformity
Policerecorded
Offense group
offenses
Convictions
Total crime
38.9 %
44.4 %
Traffic offenses
...
...
Homicide total
36.1 %
33.3 %
Homicide completed
...
...
Assault
19.4 %
19.4 %
Aggravated assault
36.1 %
44.4 %
Rape
41.7 %
47.2 %
Sexual assault
16.7 %
22.2 %
Sexual abuse of minors
36.1 %
25.0 %
Robbery
77.8 %
63.9 %
Theft total
63.9 %
50.0 %
Theft of motor vehicles
38.9 %
13.9 %
Burglary
30.6 %
16.7 %
Domestic burglary
41.7 %
5.6 %
Fraud
27.8 %
22.2 %
Computer offenses
30.6 %
30.6 %
Money laundering
52.8 %
44.4 %
Corruption
25.0 %
36.1 %
Drug offenses
50.0 %
47.2 %
Drug trafficking
...
...
Aggravated drug trafficking . . .
...

Item conformity

Median
89.9 %
...
82.1 %
...
88.2 %
93.8 %
89.9 %
84.2 %
92.6 %
94.3 %
94.2 %
86.0 %
71.1 %
84.9 %
85.3 %
100.0 %
89.7 %
89.8 %
100.0 %
...
...

Mean
87.0 %
...
82.4 %
...
85.0 %
90.1 %
89.4 %
84.2 %
90.0 %
95.1 %
93.1 %
80.5 %
77.0 %
86.4 %
81.0 %
92.2 %
90.0 %
89.4 %
93.5 %
...
...

Variation coefficient
PolicePolicerecorded recorded
offenses
suspects
0.730
0.884
2.015
1.979
0.803
0.644
0.900
0.839
1.390
1.160
1.383
1.602
1.079
0.991
1.446
1.096
1.143
1.101
0.835
1.533
0.762
0.691
0.878
1.264
0.809
0.768
0.797
0.553
1.173
1.227
2.198
2.859
2.431
3.198
1.602
1.243
1.090
1.087
1.007
0.783
1.517
1.114

Convictions
1.126
2.470
1.102
1.161
0.755
1.073
0.722
1.341
0.800
0.703
0.698
0.869
0.556
0.820
1.064
2.752
1.627
1.598
0.988
1.204
1.110

O
necessarily only be the ones that have been subject to harmonization. For money laundering on
the other hand, only the variation coefficients
(especially on police level) look problematic,
while the conformity rates are good. For an explanation, one should consider that money laundering is an offense with a relatively wide definition
which on the other hand due to its connection to
organized crime in many countries offers the
police and prosecution service relatively wide
competences, like access to special investigative
measures. Therefore, money laundering in some
countries may function as some kind of “proxy
offense” on police level, which will usually be
replaced by a charge for a more severe offense
later on (Harrendorf, 2012, p. 49).
If we now turn our attention to the conformity
rates, the first obvious result is that the item
conformity rates are much higher than the overall
conformity rates, but that is trivial (see the

definition of these rates). Apart from that, low
conformity rates do not necessarily mean that
a definition is weak and needs improvement
(Harrendorf, 2012). It may also indicate that
there is a great diversity of legal concepts for an
offense. This is, for example, known for assault/
bodily injury, for which in some jurisdictions
actual injury is necessary, while in other countries slapping and other forms of behavior that
only cause ▶ pain are sufficient, and in a third
group of countries even threats of ▶ violence are
included in the legal concept. These differences
cannot be overcome just by the use of a standard
definition.
A relatively low overall conformity rate does
also not necessarily mean that comparability is
low. One has also got to look at the items that
posed the problem: Some items on the include
and exclude lists clearly affect the “core” of an
offense (i.e., not following them will lead to

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Offense Definitions’ Impact on Criminal Justice Data Quality

clearly different results), but some items also
refer to types of behavior that occur quite seldom
in practice and are therefore situated at the fringes
of a definition. For example, bearing in mind
what has been said above about the very good
international comparability of homicide data, the
low overall conformity and item conformity rates
need explanation. As already mentioned above,
the offense is defined as intentional killing of
a person with the rules to include assault leading
to death, euthanasia, infanticide, and attempts,
but to exclude assistance with suicide. All these
rules, except for the rule to include attempts, do
not affect the core of the offense, and the item
conformity rate for the inclusion of attempts on
police level is 94 % (Harrendorf, 2012).
For sexual assault, on the other hand, the low
conformity rates are to a large extent due to
noncompliance with two important rules which
affect the “core” of the offense (exclude acts
committed without violence and acts committed
against persons under the age of consent): Sexual
assault in the ESB is defined as a violent sexual
offense, close to rape, but without penetration.
Sexual abuse of minors is a separate, third category of sexual offenses in the ESB. Less intense
sexual offenses are not collected at all. The
results show now that many countries are not
able to follow such a restrictive definition with
respect to sexual assault but report data on
a broader concept instead.
Finally, the low overall conformity rates for
burglary, domestic burglary, and theft of a motor
vehicle on convictions level are due to the fact
that these offenses in many countries do not constitute separate legal concepts: Conviction statistics are even more dependent on criminal law
than police statistics and data on these concepts
are therefore often simply unavailable.

Conclusion
Standard offense definitions are important for any
approach to compare official crime and criminal
justice statistics. The evaluation of data and metadata recorded for the fourth edition ESB shows
that this concept works well in general, but there

are areas that pose problems, often due to
very diverse legal concepts to be found across
Europe. The problems even intensify when
changing to a worldwide view (cf. Harrendorf,
Heiskanen & Malby, 2010). The differences
cannot be fully eliminated in secondary data analysis. Therefore, international ▶ victimization and
self-reported delinquency surveys should rather
be used for international comparison of actual
crime levels than criminal justice data. If one
relies to official crime and criminal justice data
for international comparison, users are advised
not to compare total crime rates but to select
individual offenses. They should rely on the
most comparable ones, like homicide, robbery,
theft, or drug offenses.

References
Aebi, M. F. (2008). Measuring the influence of statistical
counting rules on cross-national differences in
recorded crime. In K. Aromaa & M. Heiskanen
(Eds.), Crime and criminal justice systems in Europe
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Helsinki: HEUNI.
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(2010). The impact of crime victimization on qualityof-life. Journal of Traumatic Stress, 23(2), 189–197.
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global perspective: What can be learned from international survey data. In H. Kury, & E. Shea (Eds.),
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Michalos, A. C., & Zumbo, B. D. (2000). Criminal victimization and the quality-of-life. Social Indicators
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101–122.

OHS
▶ Oxford Hip Score

OISS
▶ Oxford Shoulder Instability Score

OKS
▶ Oxford Knee Score

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Old Age, Quality of Life
▶ Measuring Quality of Life of Older Adults

Older Adults in Rural Areas
▶ Rural Seniors

Older Chileans, Quality of Life
Marı́a Soledad Herrera Ponce, Marı́a Beatriz
Fernań Dez Lorca and Carmen Barros Lezaeta
Instituto de Sociologı́a, Pontificia Universidad
Católica de Chile, Santiago, Chile

Definition
A good ▶ quality of life is defined in terms of
two interrelated dimensions: satisfactory living
conditions and high degree of ▶ subjective
well-being (Palomba, 2003).
Satisfactory living conditions include the
basic dimensions of ▶ health and physical functioning and of economic conditions. This concept refers to adequate objective circumstances
and to perceptions regarding these circumstances (Lawton, 1991).
Well-being is the subjective perception of
feeling well, of feeling satisfied. It includes two
basic dimensions of well-being: ▶ satisfaction
with life and psychosocial well-being. The first
is a quite simple cognitive judgment, regarding
the individual perceptions of life satisfaction.
The other dimension consists in a balance
between positive and negative effects; it refers
to the emotional response of adapting to the
environment (Bradburn, 1969). The negative
feelings have been seen by previous authors as
indicators of emotional ▶ distress (Dowell &
Newell, 1996).

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Description
According to the literature, there are certain factors that are related to the quality of life of older
people. The social conditions of life – health and
functional capacity and economic circumstances – are key predictors of the quality of life
of older people. However, there are other social
and individual factors that are also important.
Among the factors directly related to functional
capacity are nutrition and carrying out
physical activities (Rowe & Kahn, 1997).
Numerous studies have shown that well-being
is associated to the quality of living conditions,
economic status, and health. Income has
a positive effect on well-being (Diener & Ryan,
2009), but the strength of this association
decreases as income rises. The perception of having sufficient income is more important than the
amount (Herrera et al., 2011). There is also vast
research that accounts for the positive relationship between health and well-being (Diener,
1994). Fernández Ballesteros et al. (2011), in
a study conducted in several Latin American
countries (Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, and Uruguay) and Europe (Greece,
Spain, and Portugal), conclude that good
health, being able to take care of yourself, and
having good social relationships are key to the
elderly themselves associated with a successful
old age and good living standards.
The regular practice of moderate physical
activity, such as daily regimen of walks, is
a habit that is associated to satisfactory aging.
Continued physical activity affects multiple
physical capabilities and offsets physical losses
due to aging (Hogan, 2005). Other factors mentioned as protecting the capacity for physical
functioning, as well for increasing subjective
well-being, are what Rowe and Kahn (1997)
call “engagement with life.” These are carrying
out selective activities, the existence of quality
social relations, and having a ▶ support network
(Wilhelmson et al., 2005). A number of studies
have demonstrated positive associations between
high levels of social relations (▶ social cohesion
or ▶ solidarity) and improved conditions of
health.

Older Chileans, Quality of Life

As Rowe and Kahn (1997) state, the successful
aging is also associated with carrying out selective
activities, as hobbies, vacations, going out if
home, and so on. Empirical studies in Chile have
shown that not going outside the home daily and
not reading newspapers, magazines, or books are
meaningful predictors for losing the functional
capacity and for having lower well-being (Barros
et al., 2004, 2006; Herrera, 2008).
The availability of ▶ social support fulfills
expressive, affective, and instrumental functions.
The affective and expressive functions stem from
the fact that feeling attached to others through
affective links increases ▶ self-esteem, provides
a feeling of accomplishment, and makes it possible to express negative feelings. “Instrumental
functions” refer to the fact that support is
a resource that facilitates the carrying out daily
activities and is useful in the face of whatever
may occur to the individual (Grundy, 2005). Considering social relations solely from the perspective of the availability of support seems
insufficient for explaining the impact of these
social relations on well-being. One should also
assess subjective perceptions of the quality of
relations – especially family relations, looking
at the way that family climate is perceived, in
terms of unity and ▶ trust in mutual aid, or in
negative terms of being abused, left aside, and
misunderstood (Lowenstein & Ogg, 2003). The
importance of such negative conflicting or
disturbing relations lies in fact that they can
have an even greater impact over psychosocial
well-being than positive supportive relations
(Newsom et al., 2005).
Among the individual characteristics, education is mentioned as one of the most important
protective factor of good physical and cognitive
functioning, and it has been indirectly shown to
have an effect on the quality of life of individuals
(Fernández Ballesteros, 2001). Education influences the access to knowledge, life habits, and
better income opportunities. The acquisition of
high level of education provides a cumulative
advantage that increases with the passage of
time. Educational levels are also related to what
Baltes (1993) calls the “pragmatic knowledge of
life” or the way individuals deal with the

Older Chileans, Quality of Life

situations of daily life, in which persons with
higher levels of education will be more able to
adapt themselves to changing situations
(Bandura, 1997; Lawton, 1991). To the above is
added the fact that a higher level of education of
adults offers them more tools to be mentors,
advisors, to offer emotional and financial support
to younger generations, and thus favors the linkage and quality of those relations. Other protective factors noted by Rowe and Kahn are the
perception of self- efficacy and the ability to
face situations. Lawton (1991) states that it is
the ability to adapt oneself to changing circumstances
that
permits
individuals
to
experience well-being. The perception of ▶ selfefficacy reflects belief in the ability to control the
changing demands of the environment through
adaptive actions (Bandura, 1997). This sense is
a valuable personal resource in human ▶ adaptation, since those who possess it perceive changes
that affect them as challenges which result in
actions. On the other hand, those who have low
self-efficacy perceive changes as threats, thus
creating apprehension, apathy, or despair.
Evidence About the Predictors of
Quality of Life of Older Chileans
Here are some results of a study conducted in
Chile in 2007, analyzed with multivariate regression models showing how the predictors of
quality of life mentioned above behaved. These
results are published in Herrera et al. (2011). The
data source is the First National Survey of the
Quality of Life in Old Age carried out in 2007 by
the Department of Sociology of the Pontificia
Universidad Católica de Chile and Caja de
Compensación Los Andes. The study is representative of 75 % of the population 60 years of age
and older residing in private homes.
We developed and contrasted a hypothetical
conceptual model in regard to protective
factors associated with good quality of life during
old age.
The quality of life was operationalized as perception of well-being, which was studied by
inquiring about subjective judgments of general
satisfaction with life in the last 6 months
(1 ¼ very satisfied or satisfied; 0 ¼ not very

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satisfied or not satisfied) and by means of
a ▶ distress index. The latter combines five selfreferred questions: “During the last 6 months
have you felt: tranquil, depressed, lonely, energetic, nervously?” The response are from 1 (quite
often), 2 (sometimes), to 3 (almost never).
A principal components analysis confirmed unidimensionality with similar weights, so, after
inverting the two positive items, a simple average
is calculated.
Almost all of the variables hypothesized as
predictive factors effectively had direct associations with the subjective measures of well-being,
except education. Education is correlated with
various other predictive factors such as income,
physical functioning, self-efficacy, and carrying
out selective activities. So, having higher
education will be related to quality of life in an
indirect way.
As expected, life conditions (perception of
income and physical functioning) were strongly
associated to the perception of well-being of
elderly persons, but by themselves explain little.
It is also important to emphasize that when we
controlled the other predictive variables by
income and physical functioning, almost all the
variables maintained their importance.
The weights of the other predictive factors were
distinct, depending upon how well-being was measured, as satisfaction with life or as distress.
As predicted, having good social relations and
having an active aging were associated with better
well-being. Having good family relations was
significant both to satisfaction and distress, but
especially for the latter. Not having unsatisfactory
social relations was significant only to satisfaction
with life. Having social support was related to
lower distress, but not with greater satisfaction.
People with more social support were not necessary happier, in part because this could threaten
their sense of independence. However, the distress
reduced if the individual felt that he or she could
obtain support in case of difficulties.
The selected activity of reading newspapers,
books, or magazines more than once a week was
associated to satisfaction and low distress, even
when it was controlled by income and physical
functioning.

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In regard to the control variables, one should
note the following: there was no direct relation
between gender and satisfaction; but women
experienced more distress. At the bivariate
level, we have not found associations between
age and well-being, but we did find it when we
added other variables to the model. Older people
had a higher probability of reporting satisfaction
with life and less distress. We could not observe
this association at the bivariate level, because age
was negatively associated with several variables
that were positively associated to well-being, as
physical activity, going outside home, selfefficacy, and physical functioning.
In summary,
– The study confirmed that life conditions are
indeed related to the level of well-being in old
age, but the variations in this measure are
better explained by incorporating other factors
such as self-efficacy and the quality of family
relations.
– The study evidenced the importance of physical activity for maintaining good functional
capacity, reiterating what has been found in
almost all other studies of this type.
– Carrying out activities such as going out of the
home and reading are more associated to
maintaining good physical functioning than
for feeling satisfied or for possessing low distress – although one should note that the direction of the relation may be in the sense that
individuals who have greater functional
capacity are those who can maintain a more
active lifestyle.
– The quality of social relations has disparate associations, depending upon how this is measured
and what quality of life dimension one is measuring. In this study we used three indicators:
potential availability of social support,
a subjective assessment of the quality of family
functioning, and the existence of unsatisfactory
relations with the children and persons with
whom elderly adults live. The latter is one of
the most important variable associated both to
physical functioning as well as the perception of
satisfaction in the study of Newsom et al. (2005).
Variations in distress are, however, better
explained by the quality of family relations and

Older Chileans, Quality of Life

by the availability of social support.
The perception that one could have support in
case of difficulties is more important to reduce
the sense of distress, especially when there is
more probability to experience functional
dependence.
– Another of the most notable outcomes of the
study was to show the importance of
self-efficacy – especially on life satisfaction.
Self-efficacy is an individual characteristic
that is accumulated throughout life, which is
very much related to the educational level of
individuals.

Cross-References
▶ Affect Balance Scale
▶ Affective Component of Happiness
▶ Elderly Activity and Engagement with Life
▶ Emotional Well-Being
▶ Family Conflicts
▶ Family Life Quality, Measures of
▶ Family Support
▶ Happiness of Older People in Italy and Cuba
▶ Health and Well-Being of Older Europeans
▶ Negative Affect and Daily Stressors in Older
Adults
▶ Quality of Life for Older Adults, an Integrated
Conceptual Model
▶ Subjective Health and Subjective Well-Being

References
Baltes, P. (1993). The aging mind: Potentials and limits.
The Gerontologist, 33, 580–594.
Bandura, A. (1997). Self-efficacy: The exercise of control.
New York: W.H. Freeman.
Barros, C., Forttes, A., & Avende
no, C. (2004).
Componentes de la calidad de vida del adulto mayor
y factores asociados (Informe técnico Fondecyt
1020643). Santiago de Chile, Chile: Universidad
Católica de Chile.
Barros, C., Forttes, A., & Herrera, M. S. (2006).
Predictores de un buen envejecimiento entre los
adultos mayores afiliados a la Caja de Compensación
de Los Andes. Santiago de Chile, Chile: Universidad
Católica, Caja Los Andes.
Bradburn, N. (1969). The structure of psychological wellbeing. Chicago: Aldine.

Older Couples
Diener, E. (1994). El bienestar subjetivo. Intervención
psicosocial. Revista Sobre Igualdad y Calidad de
Vida, 3(8), 67–113.
Diener, E., & Ryan, K. (2009). Subjective well-being:
A general overview. South African Journal of Psychology, 39(4), 391–406.
Fernández Ballesteros, R. (2001). Environmental conditions, health and satisfaction among the elderly: Some
empirical results. Psicothema, 13, 40–49.
Fernández-Ballesteros, R., Zamarrón, M., Dı́ez-Nicolés, J.,
López-Bravo, M., Moline, M., & Schettini, R. (2011).
Productivity in old age. Research on Aging, 33,
205–226.
Grundy, E. (2005). Reciprocity in relationships: Socioeconomic and health influences on intergenerational
exchanges between third age parents and their adult
children in Great Britain. The British Journal of Sociology, 56(2), 233–255.
Herrera, M. S. (2008). Predictores de calidad de vida
entre los adultos mayores Chilenos. Seminario
primera encuesta de calidad de vida en la vejez
2007. Santiago de Chile, Chile: Centro de Extensión
Pontificia Universidad Católica de Chile.
Herrera, M. S., Barros, C., & Fernéndez, B. (2011). Predictors of quality-of-life in old age: A multivariate
study in Chile. Journal of Population Ageing, 4(3),
121–139.
Hogan, M. (2005). Physical and cognitive activity
and exercise for older adults: A review. International
Journal of Aging & Human Development, 60(2),
95–126.
Lawton, M. (1991). A multidimensional view of
quality-of-life in frail elders. In J. E. Birren (Ed.),
The concept and measurement of quality-of-life in the
frail elderly (pp. 3–27). San Diego, CA: Academic
Press.
Lowenstein, A., & Ogg, J. (2003). OASIS final report.
Haifa, Israel: Center for Research and Study of Aging.
Mc Dowell, I., & Newell, C. (1996). Mental status testing.
Measuring health. A guide for rating scales and questionnaires (pp. 314–322). New York: Oxford University Press.
Newsom, J., Rook, K., Nishishiba, M., Sorkin, D., &
Mahan, T. (2005). Understanding the relative importance of positive and negative social exchanges:
Examining specific domains and appraisals. Journals
of Gerontology: Psychological Sciences, 60B(6),
P304–P312.
Palomba, R. (2003). Recomendaciones para realizar
investigaciones sobre redes de apoyo y calidad de
vida: Agenda de investigación y métodos
e
instrumentos
para
estudios
cualitativos
y cuantitativos. Notas de Población (CEPAL/
CELADE), 77, 251–262.
Rowe, J. W., & Kahn, R. L. (1997). Successful aging. The
Gerontologist, 37, 433–440.
Wilhelmson, K., Andersson, C., Waerm, M., & Allebeck,
P. (2005). Elderly people’s perspectives on quality-oflife. Ageing & Society, 25(4), 585–600.

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Older Couples
Ruth Walker
Southgate Institute for Health, Society and
Equity, Flinders University, Adelaide,
SA, Australia

Synonyms
Elderly couples; Late-life marriage

Definition
In terms of research on older couples and wellbeing, most studies have focused on traditional
heterosexual marriages of around 40 years duration. Consensus over what constitutes a marriage
relationship and whether the key factors relating
to older couples apply to people in nontraditional
but long-lasting relationships (i.e., de facto or
consensual unions) or in non-heterosexual relationships are important considerations for future
research.

O
Description
The complex nature of late-life husband and wife
relationships is increasingly recognized as an
important factor contributing to healthy aging
and quality of life (Walker & Luszcz, 2010).
The long-term marriage relationship is highly
relevant in terms of the ▶ health and social, emotional, financial, and practical needs of older people, yet the dynamics of this relationship remain
poorly understood (Ray, 2000). Marriage provides spouses their primary source of ▶ social
support and economic stability, which in turn
have been shown to be associated with physical
▶ health (Stimpson & Peek, 2005). Research on
older couples has traditionally depicted a positive
view of late-life marriage, whereby older couples
have been found to be better at managing conflict
and demonstrate more positive interactions

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compared to younger couples (Carstensen, Graff,
Levenson, & Gottman, 1996; Gottman &
Notarius, 2000). The literature on older couples
also tends to be dominated by research which
emphasizes the caregiving dimension of one
spouse, particularly in terms of dementia ▶ caregiver burden [e.g., Torti, Gwyther, Reed, Friedman, & Schulman, 2004].
Research on older couples is increasingly
focusing on the notion that individual development and well-being influences and is influenced
by “close others” (notably the spouse) and that
individual outcomes might be shaped by
sociocontextual factors such as the marital relationship [for review, see (Hoppmann & Gerstorf,
2009)]. That is, spousal interrelations have the
potential to “coproduce important aging outcomes concerning cognitive functioning, wellbeing and health” (Hoppmann & Gerstorf, 2009,
p. 449). Socioemotional selectivity theory
(Carstensen, 1993) offers a potential rationale
for the role that spousal interrelations may have
on quality of life. This theory suggests that the
marriage relationship becomes the most salient in
late life, a time which is characterized by
a conscious narrowing of social networks, with
close interpersonal relationships such as those
with a spouse becoming most relevant in terms
of support and satisfaction (Lang, 2001). It thus
follows that the socioemotional tone within the
marriage may therefore be important in affecting
▶ health and quality of life.
Until recently, the considerable body of
research on late-life spousal interrelations and
their implications for well-being has been crosssectional. This research has found strong evidence for similarities among couples in levels of
well-being and depression even after controlling
for ▶ health and sociodemographic covariates
(Bookwala & Schulz, 1996; Tower & Kasl,
1995). In terms of an explanation for the concordance of depression among older spouses, several
studies have posited the theory of “emotional or
▶ affective contagion.” Such contagion may
arise when people interact in a close relationship,
such as marriage, are interdependent, and, to
a certain extent, control each other’s outcomes,
thus leading at times to a convergence in

Older Couples

emotional patterns such as negative ▶ mood
(Bookwala & Schulz, 1996; Dufouil &
Alperovitch, 2000; Goodman & Shippy, 2002;
Kivela, Luukinen, Viramo, & Koski, 1998;
Stimpson, Peek, & Markides, 2006; Tower &
Kasl, 1995, 1996a). Evidence from studies of
midlife spouses has shown transmission of
▶ mood in terms of the day-to-day flow of affect
between spouses, and this shows a genderspecific pattern (Holahan et al., 2007). In particular, wives are often more responsive to their
husbands’ negative emotional states than vice
versa (Bolger, Delongis, Kessler, & Wethington,
1989; Larson & Almeida, 1999). Husbands have
thus been described as “senders” of negative
emotion and wives as “receivers” (Larson &
Almeida, 1999). Some authors have suggested
that the mechanism underpinning these gender
differences may reflect a greater focus on autonomy for men and on interpersonal relationships
for women (Quirouette & Gold, 1992). That is,
wives may be particularly vulnerable to the harmful effects of marital disharmony or husbands’
poor mental health status, unlike husbands
where independence from wives (or self-sufficiency) might be protective. When the husband
does draw on his wife for emotional support,
however, there is evidence that he may be
particularly susceptible to poor mental health
(Tower & Kasl, 1996b).
Empirical research among late-life couples
seeks to more fully determine how well-being of
the marital partner influences and is influenced by
the other partner. Such studies are applying
a dyadic approach using longitudinal data from
both members of a couple and take into account
the interdependence of spouses’ experiences in
terms of data collection and analysis by examining both individual-level and couple-level variables. Recent use of dynamic dual change score
models (McArdle & Hamagami, 2001) can
reveal the dynamics (changes) for both the partners
and the couple, within or across domains of health
over time. Such research has found spousal interrelations across several domains, including changes in
social activities (Hoppmann, Gerstorf, & Luszcz,
2008), cognition (Gruber-Baldini, Schaie, &
Willis, 1995; Gerstorf, Hoppmann, Anstey &

Older Couples

Luszcz, 2009), and subjective well-being (including
negative as well as positive emotional states)
(Tower & Kasl, 1996b; Walker, Luszcz, Gerstorf, &
Hoppmann, 2010; Gerstorf, Hoppmann, Kadlec &
McArdle, 2009). For example, Walker et al. (2010)
examined spousal interrelations in late-life subjective well-being (SWB), as indexed by morale. This
study examined whether transmission in ▶ mood,
as has been described in midlife couples, can also be
found in long-term longitudinal data from older
couples, taking into account possible confounds
including differences in age, ▶ education, health,
and marital characteristics (number of children and
length of marriage). This study found that timelagged spousal dynamics did indeed exist in that
while SWB generally declined over time, husbands
whose wives reported higher initial SWB showed
relatively shallower decline over time relative to
husbands whose wives reported lower initial SWB
levels. The reverse pattern of influence was not
apparent for husbands’ levels of SWB influencing
wives. These findings illustrate empirically that
close relationships shape individual developmental
outcomes and suggest that spouses play an important role in determining well-being. The directionality of findings is interesting in that unlike for
research with midlife couples (where husbands are
senders, and wives receivers), in terms of positive
subjective well-being (in this case morale), wives
levels dictate subsequent levels in husbands. One
possible reason for different spousal dynamics in
old age compared to midlife may be that husbands
are particularly affected by the kin-keeping and
socially active roles of their wives in times when
their own work-related social networks and roles
may be diminished because of retirement. Another
factor may be that, in line with theories of emotion
regulation in late life (Carstensen, 1993), husbands
may feel closer to their wives post-retirement and
thereby be more susceptible to the often emotionally more expressive behaviors of wives
(Carstensen, Graff, Levenson, & Gottman, 1996).
It may also be that the directionality of spousal
interrelations in SWB transmission (i.e., husbands
to wives or vice versa) might be different for negative (i.e., depression) versus positive (i.e., morale)
markers. It is for future research to examine these
and other possible factors in greater detail.

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Finally, it is important to note that the majority
of research on older couples is limited in its
generalizability being conducted predominantly
among heterosexual, Anglo samples in first-time
traditional marriages. The extent to which the
dynamics described above apply to different
types of older couples (culturally and linguistically diverse, same-sex relationships, second
marriages) requires further study.

Cross-References
▶ Affective Contagion
▶ Caregiver Burden
▶ Education
▶ Health
▶ Mood
▶ Social Support
▶ Subjective Well-Being (SWB)

References
Bolger, N., Delongis, A., Kessler, R. C., &
Wethington, E. (1989). The contagion of stress across
multiple roles. Journal of Marriage and the Family,
51, 175–183.
Bookwala, J., & Schulz, R. (1996). Spousal similarity in
subjective wellbeing: The Cardiovascular Health
Study. Psychology and Aging, 11, 582–590.
Carstensen, L. L. (1993). Motivation for social
contact across the life span: A theory of
Socioemotional Selectivity. In T. B. Sonderegger
(Ed.), Nebraska symposium on motivation
(pp. 209–254). Nebraska, Lincoln: University of
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Carstensen, L. L., Graff, J., Levenson, R. W., & Gottman,
J. M. (1996). Affect in intimate relationships: The
developmental course of marriage. In C. Magai &
S. H. McFadden (Eds.), Handbook of emotion, adult
development and aging (pp. 227–247). San Diego:
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Dufouil, C., & Alperovitch, A. (2000). Couple
similarities for cognitive functions and psychological health. Journal of Clinical Epidemiology, 53,
589–593.
Gerstorf, D., Hoppmann, C. A., Anstey, K. J., & Luszcz,
M. A. (2009). Dynamic links of cognitive functioning
among married couples: Longitudinal evidence from
the Australian Longitudinal Study of Aging. Psychology and Aging, 24, 296–309.

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Gerstorf, D. A., Hoppmann, C. A., Kadlec, K. M., &
McArdle, J. J. (2009). Memory and depressive symptoms are dynamically linked among married couples:
Longitudinal evidence from the AHEAD study. Developmental Psychology, 45, 1595–1610.
Goodman, C. R., & Shippy, R. A. (2002). Is it contagious?
Affect similarity among spouses. Aging & Mental
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Gottman, J. M., & Notarius, C. I. (2000). Decade review:
Observing marital interaction. Journal of Marriage
and the Family, 62, 927–947.
Gruber-Baldini, A. L., Schaie, K. W., & Willis, S. L.
(1995). Similarity in married couples: A longitudinal
study of mental abilities and rigidity-flexibility. Journal of Personality and Social Psychology, 69,
191–203.
Holahan, C. J., Moos, R. H., Moerkbak, M. L., Cronkite,
R. C., Holahan, C. K., & Kenney, B. A. (2007). Spousal similarity in coping and depressive symptoms over
10 years. Journal of Family Psychology, 21, 551–559.
Hoppmann, C. A., & Gerstorf, D. (2009). Spousal interrelations in old age: A mini review. Gerontology, 55,
449–459.
Hoppmann, C. A., Gerstorf, D., & Luszcz, M. A. (2008).
Spousal activity trajectories in the Australian
Longitudinal Study of Aging in the context of cognitive, physical and affective resources. Journals of
Gerontology: Psychological Sciences Social Science,
63, 41–50.
Kivela, S.-L., Luukinen, H., Viramo, P., & Koski, K.
(1998). Depression and couples: Depression in elderly
spouse pairs. International Psychogeriatrics, 10,
329–338.
Lang, F. R. (2001). Regulation of social relationships in
later adulthood. Journals of Gerontology, 56B,
321–326.
Larson, R. W., & Almeida, D. M. (1999). Emotional
transmission in the daily lives of families: A new paradigm for studying family process. Journal of Marriage and the Family, 61, 5–20.
McArdle, J. J., & Hamagami, F. (2001). Latent difference
score structural models for linear dynamic analyses
with incomplete longitudinal data. In L. M. Collins &
A. G. Sayer (Eds.), New methods for the analysis of
change (pp. 137–176). Washington: American Psychological Association.
Quirouette, C., & Gold, D. P. (1992). Spousal characteristics as predictors of well-being in older couples.
International Journal of Aging & Human Development, 34, 257–269.
Ray, M. (2000). Older women, long-term marriage and
care. In M. Bernard, J. Phillips, L. Machin, & V.
Harding Davies (Eds.), Women ageing: Changing
identities, challenging myths (pp. 148–167). London:
Routledge.
Stimpson, J. P., & Peek, M. K. (2005). Concordance of
chronic conditions in older Mexican American couples. Preventing Chronic Disease: Public Health
Research, Practice and Policy, 2, 1–7.

Older People in Rural Areas
Stimpson, J. P., Peek, M. K., & Markides, K. S. (2006).
Depression and mental health among older Mexican
American spouses. Aging & Mental Health, 10,
386–393.
Torti, F. M., Jr., Gwyther, L. P., Reed, S. D., Friedman,
J. Y., & Schulman, K. A. (2004). A multinational
review of recent trends and reports in dementia caregiver burden. Alzheimers Disease and Associated Disorders, 18, 99–109.
Tower, R. B., & Kasl, S. V. (1995). Depressive symptoms
across older spouses and the moderating effect of
marital closeness. Psychology and Aging, 10,
625–638.
Tower, R. B., & Kasl, S. (1996a). Depressive symptoms
across older spouses: Longitudinal influences. Psychology and Aging, 11, 683–697.
Tower, R. B., & Kasl, S. V. (1996b). Gender, marital
closeness, and depressive symptoms in elderly couples. Journal of Gerontology: Psychological Sciences,
51, 115–129.
Walker, R., & Luszcz, M. (2010). The health and relationship dynamics of late-life couples: A systematic
review of the literature. Ageing and Society, 29,
455–480.
Walker, R., Luszcz, M. A., Gerstorf, D. A., & Hoppmann,
C. A. (2010). Subjective well-being dynamics in couples from the Australian Longitudinal Study of Aging.
Gerontology, 57, 153–160.

Older People in Rural Areas
▶ Rural Seniors

Oldest Old, Life Satisfaction, and
Health
Wencke Gwozdz1 and Alfonso Sousa-Poza2
1
Department of Intercultural Communication &
Management, Copenhagen Business School,
Frederiksberg, Denmark
2
Department of Household and Consumer
Economics and Gender Economics, University of
Hohenheim, Stuttgart, Germany

Synonyms
Fourth age and well-being; Survey of Health
Ageing and Retirement in Europe (SHARE)

Oldest Old, Life Satisfaction, and Health

4485

Definition
Several definitions already exist for the “oldest
old” or “fourth age,” which comprises the most
elderly of the older generation. One populationbased possibility is to define the transition
between the third and fourth age as the chronological age at which 50 % of the birth cohort is no
longer alive, which in developed countries, takes
place at around 75 years of age (Baltes & Smith,
2003). Being older than 85 years of age is also
often used as a delineation.

Description
In the industrialized world, the oldest old are
gaining increasing attention merely because of
this population group’s rapid growth. In fact, the
progressive demographic aging of the older

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population may in itself be one of the most
remarkable demographic developments, one
that, because of falling fertility rates and lengthening life expectancies, is expected to continue
indefinitely (United Nations, 2004). About 4.1 %
of the European population is currently aged 80
and above – with annual growth rates worldwide
averaging 3.4 % (United Nations, 2004) – and
this number is projected to rise to 10.1 % by 2050.
Based on these projections, by 2050, there will be
113 million oldest old in more developed countries and 265 million in less developed countries.
The well-being of this population group has
received considerable attention in gerontological
and medical research; however, the findings on
the relationship between ▶ life satisfaction
and age across the life cycle are inconsistent
(see Table 1 for an overview). Whereas some
studies observe a U-shaped relation (e.g., Clark,
2007; Easterlin, 2006; Frey & Stutzer, 2002),

Oldest Old, Life Satisfaction, and Health, Table 1 Recent research on life satisfaction and aging
Study
Blanchflower
and Oswald
(2008)
Chen (2001)

Data
US GSS (1972–2006)/
Eurobarometers (1976–2002)

Age
groups Results
All
U-shaped relation between life satisfaction and age

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Clark (2007)
Deaton (2007)

Survey of health and living status
of the elderly in Taiwan (1989,
1993)
BHPS (1991–2004)
World gallup poll (2006)

Easterlin
(2006)
Fischer (2009)

World value survey (1997–2001)

All

GSOEP (1994–2006)

All

GSOEP (1984–2007)

All

Frey and
Stutzer (2002)
Gwozdz and
Sousa-Poza
(2010)
Mroczek and
Spiro (2005)
Schilling
(2005)
Van
Landeghem
(2012)

60+

Life satisfaction decreases with old age

16–64 U-shaped relation between life satisfaction and age
All
Age has an inconsistent relation with happiness (analysis
does not, however, include covariates)

Hyperbolic relation between life satisfaction and age;
decline after 83 years

Lowest absolute levels of life satisfaction for oldest old,
including cohort effects that lead to stable life
satisfaction across the life span
Veterans affairs normative Ageing 40–85 SWB rises in the early 60s and then declines
study (1978–1999)
GSOEP (1984–1999)
45–90 Decline in life satisfaction from 60 onwards
Decrease in life satisfaction after midlife

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Oldest Old, Life Satisfaction, and Health

Oldest Old, Life Satisfaction, and Health, Table 2 Recent research on life satisfaction and health among the oldest
old
Study
Berg et al.
(2006)
Borg et al.
(2006)
Bourne et al.
(2010)
Bowling and
Farquhar
(1996)
Chen and Short
(2008)
Gwozdz and
Sousa-Poza
(2010)
Inal et al.
(2007)
Li (2005)
Smith et al.
(2002)

Data
OCTO-twin study (1991)
European study of ageing well
(2001/2002)
Stratified random sample of 2,000
elderly men in Jamaica (2007)

Age
groups Results
80+
Objective health measures, unlike subjective health
measures, do not affect life satisfaction
65–89 Negative effect of subjective health on life satisfaction
60+

Three samples of elderly people in 85+
London and Essex (1987–1990)
Determinants of healthy longevity
in China (DHLC) survey (1998/
2000)
GSOEP (1994–2006) and share
(2004)
Sample of 133 institutionalized
Turkish individuals
Chinese longitudinal healthy
longevity survey (2000/2002)
Berlin aging study (1993–2000)

Life satisfaction was not correlated with the self-rated
health status of elderly men in Jamaica nor was health
status associated with life satisfaction
Subjective health is a strong predictor of life satisfaction

80+

Positive effect of subjective health on life satisfaction

75+

Perceived health is a strong predictor for life satisfaction
but objective health is not

65+

Activities that enhance health are significantly related to
life satisfaction
Perceived health is a strong predictor of life satisfaction

80+
70+

other studies find no relationship or a hyperbolic
function (Deaton, 2007; Fischer, 2009; Gwozdz &
Sousa-Poza, 2010). These latter studies, as well as
those that focus only on the older population group,
show more consistent results: they agree that the
oldest old tend to be at risk of decreased life
satisfaction but set the turning point at various
ages from 60 to 83 years (e.g., Chen, 2001;
Mroczek & Spiro, 2005; Schilling, 2005). Older
gerontological studies, on the other hand, were
unable to confirm any relationship between age
and life satisfaction; however, result comparability
is also often hampered by differences in research
design and data sets.
The body of gerontological and medical
research investigating the effect of ▶ health on
life satisfaction is relatively large and includes
older studies and newer studies by Berg, Hassing,
MCClearn and Johansson (2006); Borg, Hallberg
and Blomquist (2006); Bourne, Morris and
Eldemire-Shearer (2010); Bowling and Farquhar
(1996); Chen and Short (2008); Inal, Subasi, Ay,
and Hayran (2007); Li (2005); and Smith,

Subjective health is a more powerful predictor of
well-being than objective measures

Borchelt, Maier, and Jopp (2002). The most
recent studies and their primary results are listed
in Table 2. Overall, the findings suggest that
health does affect life satisfaction, although
a distinction must be made between self-rated
(subjective) and measured (objective) health.
The prevalence of objective health problems
is high among old people. For example, the
▶ Survey of Health, Ageing and Retirement
in Europe (SHARE), which collected data on
the 50+ age group in 12 countries, shows that
about 40 % of the older population group experiences activity limitations because of health problems (Alcser & Borsch-Supran, 2005). Almost
50 % experience long-term health problems
including ▶ arthritis, diabetes, heart diseases,
hypertension, and high cholesterol, and 40 %
reported two or more chronic diseases.
Comparing the prevalence of health problems
among the youngest old (aged 50–74 years) with
those of the oldest old in ▶ Germany (SHARE),
Gwozdz and Sousa-Poza (2010) find that serious
health problems like heart attacks, strokes,

Oldest Old, Life Satisfaction, and Health

diabetes, arthritis, Parkinson’s, and cataracts
are much more likely among the oldest old: over
20 % experienced a heart attack, 20 % had
cataracts, 18 % had diabetes, and 18 % suffered
from arthritis.
According to the SHARE data, 40 % of
the oldest old evaluate their own health subjectively
as less than good, and 10 % of this population group
even rates it poor (Alcser & Borsch-Supran, 2005).
A comparison of self-rated health between
the youngest and the oldest old again reveals
statistically significant differences (Gwozdz &
Sousa-Poza, 2010).
All studies except one (Bourne et al., 2010)
show a strong positive association between life
satisfaction and subjective health. Interestingly,
however, in line with the majority of gerontological studies (e.g., Berg et al., 2006; Smith et al.,
2002), objective health does not appear to play an
important role. Gwozdz and Sousa-Poza (2010)
identify only one noteworthy exception, that is,
suffering from Parkinson’s disease, an illness that
often leads to severe loss of independence and
decreases life satisfaction.

Discussion
In old age, health does affect life satisfaction;
however, according to the majority of studies,
this association is primarily attributable to subjective health and barely linked to objective
health. Several explanations have been suggested
for this finding:
1. Measurement of objective health: Most data
sets do not account for the severity of an illness, that is, they only record the existence of
a health problem, making it difficult to draw
conclusions on the effects of objective health
on life satisfaction.
2. ▶ Social participation: Life satisfaction is
strongly influenced by social participation so
objective health may only affect functional
health (e.g., hearing, physical mobility)
indirectly and with a time lag, which in turn
influences social participation. Subjective
health, in contrast, influences social participation directly.

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3. Discrepancy theory: Life satisfaction is maximized when expectations and aspirations are
adjusted to actual achievements (Michalos,
1979), implying a lower life satisfaction
when achievements are below expectations.
Moreover, “as morbidity and disability are
common in late life, with health-related
impairments, prospects on health and functioning are lower. A plausible life satisfaction
in old age may become more dependent on
other factors including changing preferences
about what really matters in old age” (Berg
et al., 2006, p. 262). In fact, some studies
indicate that older age groups show lower
discrepancies between aspirations and
achievements, which goes hand in hand with
the “stability despite loss paradox”
(Kunzmann, Little, & Smith, 2000) that
describes old people’s ability to adapt to
worsening (health) conditions.
4. ▶ Social comparison: Individuals compare
themselves to others; for example, peers
evaluate their own life satisfaction. Because
the peers of older people are more likely to be
other older people, the oldest old are more
likely to compare themselves to other oldest
old than to younger generations. Hence,
a rather fit and healthy person for this age
group might perceive her/his health as
excellent, a phenomenon also called the
“satisfaction paradox” (Walker, 2005).
Indeed, research does indicate that older people tend to use downward social comparison,
that is, to peers or other groups that are worse
off than themselves.
5. ▶ Life expectancy: There is also evidence that
happy people might live longer (e.g., Danner,
Snowdown, & Friesen, 2001), which would
imply that the oldest old analyzed in research
might per se be happier.

References
Alcser, K.H. & Borsch-Supran, A. (2005). Health, Aging
and retirement in Europe: First results from the survey
of health, ageing and retirement in Europe. Mannheim
Research Institute for the Economics of Aging (MEA),
Mannheim.

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Baltes, M. M., & Smith, J. (2003). New frontiers in the
future of aging: From successful aging of the
young Old to the dilemma of the fourth Age.
Gerontology, 49, 123–135.
Berg, A. I., Hassing, L. B., MCClearn, G. E., & Johansson,
B. (2006). What matters for life satisfaction in the
oldest-old? Aging & Mental Health, 10(3), 257–264.
Blanchflower, D.G. & Oswald, A.J. (2008). Is well-being
U-shaped over the life cycle? Social Science &
Medicine (forthcoming).
Borg, C., Hallberg, I. R., & Blomquist, K. (2006). Life
satisfaction among older people (65+) with reduced
self-care capacity: The relationship to social, health,
and financial aspects. Journal of Clinical Nursing,
15, 607–618.
Bourne, P. A., Morris, C., & Eldemire-Shearer, D. (2010).
Happiness among older men in Jamaica: Is it a health
issue? Asian Journal of Medical Sciences, 2(1), 25–36.
Bowling, A., & Farquhar, M. (1996). Associations with
changes in life satisfaction among three samples of
elderly people living at home. International Journal
of Geriatric Psychiatry, 11, 1077–1087.
Chen, C. (2001). Aging and life satisfaction. Social
Indicators Research, 54, 57–79.
Chen, F. & Short, S.E. (2008). Household context and
subjective well-being among the oldest-old in China.
Journal of Family Issues (forthcoming).
Clark, A.E. (2007). Born to be mild? Cohort effects don’t
(fully) explain why well-being is u-shaped in age. IZA
Working Paper No. 3170.
Danner, D. D., Snowdown, D. A., & Friesen, W. V.
(2001). Positive emotions in early life and longevity:
Findings from the Nun study. Journal of Personality
and Social Psychology, 80, 804–813.
Deaton, A. (2007). Income, aging, health and well-being
around the world: evidence from the Gallup World
Poll. NBER Working Paper No. 13317.
Easterlin, R. (2006). Life cycle happiness and its sources:
Intersections of psychology, economics, and demography. Journal of Economic Psychology, 27, 463–482.
Fischer, J. (2009). Happiness and age cycles - return to
start. . .. MPRA Paper No. 15249.
Frey, B. S., & Stutzer, A. (2002). Happiness and
economics. Princeton and Oxford: Princeton University Press.
Gwozdz, W., & Sousa-Poza, A. (2010). Ageing, health
and life satisfaction of the oldest old: An analysis for
Germany. Social Indicators Research, 97(3), 397–417.
Inal, S., Subasi, F., Ay, S. M., & Hayran, O. (2007). The
link between health-related behaviours and life satisfaction in elderly individuals who prefer institutional
living. BMC Health Services Research, 7, 30–37.
Kunzmann, U., Little, T. D., & Smith, J. (2000). Is agerelated stability of subjective well-being a paradox?
Cross-sectional and longitudinal evidence from the Berlin aging study. Psychology and Aging, 15(3), 511–526.
Li, Q. (2005). Subjective well-being and mortality
in Chinese oldest old. MPIDR Working Paper
No. 2005-011.

Old-Time Music
Michalos, A. C. (1979). Multiple discrepancies theory
(MDT). Social Indicators Research, 16, 347–413.
Mroczek, D. K., & Spiro, A. (2005). Change in life
satisfaction during adulthood: Findings from the
veterans affairs normative aging study. Journal of
Personality and Social Psychology, 88(1), 189–202.
Schilling, O. K. (2005). Cohort- and age-related decline in
elder’s life satisfaction: Is there really a paradox?
European Journal of Aging, 2, 254–263.
Smith, J., Borchelt, M., Maier, H., & Jopp, D. (2002).
Health and well-being in the young old and oldest
old. Journal of Social Issues, 58(4), 715–732.
United Nations (2004). World population to 2300. United
Nations department of economic and social affairs/
population division, ST/ESA/SER.A/236. United
Nations, New York.
Van Landeghem, B. (2012). A test for the convexity of
human well-being over the life cycle: Longitudinal
evidence from a 20-year panel. Journal of Economic
Behavior & Organization, 81(2), 571–582.
doi:10.1016/j.jebo.2011.08.001.
Walker, A. (2005). A European perspective on
quality-of-life in old age. European Journal of Ageing,
2, 2–12.

Old-Time Music
▶ Folk Music

Olson, Mancur
Alex C. Michalos
University of Northern British Columbia, Prince
George, BC, Canada
(residence) Brandon, MB, Canada

Birth, Education, Work History,
and Main Contributions
Mancur Olson (1932–1998) was a pioneer in
studies of ▶ social indicators of well-being or
▶ quality of life research. He was born in Grand
Forks, North Dakota, earned a BS from North
Dakota Agricultural College (1954), an MA
from Oxford University (1956), and a PhD from
Harvard University (1963). From 1960 to 1967
he taught at Princeton University and from 1969

Oncology

to 1998 at the University of Maryland in
College Park.
From 1967 to 1969 he served as Deputy Assistant Secretary, US State Department of Health,
Education and Welfare. These were the last
2 years that Lyndon B. Johnson was President
of the United States, with his notion of a Great
Society very much alive. In support of Johnson’s
idea, among other things, the US Department of
Health, Education and Welfare produced
Toward a Social Report in 1969 (US Department
of Health, Education and Welfare,1969), largely
authored by Olson. In broad strokes it defined
social indicators, sketched a framework for
assembling social indicators into a social report,
and distinguished such reports from social
accounts. Although it was a relatively small
monograph, it became a workman’s handbook
for North American researchers in the field for
several years after its appearance.
Olson was one of the Charter Members of the
Public Choice Society, as was the author of this
essay. Later he served a term as its President.
I met him at some conference at the University
of Maryland in 1972 and we became friends. At
the time I was working on my Foundations of
Decision-Making (Michalos, 1978). I had read
and admired Olson’s classic book (which was
also his PhD dissertation) on the Logic of Collective Action (Olson, 1965). Because he was an
active member of the steering committee of the
US Social Science Research Council’s Center for
Coordination of Research on Social Indicators,
besides our interest in formal issues of public
choice, we shared an interest in the social indicators movement. As explained in Michalos, 2005,
out of our shared interests and friendship, the
journal ▶ Social Indicators Research emerged.
Among his many accomplishments, he served
as President of the Eastern Economic Association
and of the Southern Economic Association and
as Vice-President of the American Economic
Association. He held a Distinguished Fellowship
at the US Institute of Peace and was an Honorary
Fellow of University College, Oxford. The American Political Science Association honored him by
establishing the Olson Award for the best PhD
dissertation in political economy. Near the end of

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his obituary in The Independent, its author
described Olson as a “most humble, personally
self-effacing, anglophile, delightful, modest economist.” To all those accurate descriptors, I would
only add (as the evidence presented here shows)
that he was a first-rate scholar and an important
promoter of ▶ quality of life research.

References
Michalos, A. C. (1978). Foundations of decision making.
Ottawa, Canada: Canadian Library of Philosophy.
Michalos, A. C. (2005). Social indicators research: A case
study of the development of a journal. In
A. C. Michalos (Ed.), Citation classics from social
indicators research: The most cited articles edited
and introduced by Alex C. Michalo. Dordrecht:
Springer.
Olson, M. (1965). Logic of collective action. Cambridge:
Harvard University Press.
US Department of Health, Education and Welfare. (1969).
Toward a social report. Washington, DC: US
Government Printing Office.

Omoiyari
▶ Japan, Quality of Life

Oncology
Ulrich Wiesmann
Institute for Medical Psychology, Greifswald
University Medicine, Greifswald, Germany

Definition
Oncology is a branch of internal medicine
that deals with etiology and therapy of
tumors (neoplasms). A tumor is an abnormal
swelling – an abnormal mass of cells resulting
from excessive cellular multiplication – which
can be benign, premalignant, or malignant.
More specifically, oncology is concerned
with the development, diagnosis, and treatment

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(curative treatment, rehabilitation, aftercare,
palliative treatment) of malignant tumors
(cancer) and their prevention. One major
health-care objective is to maintain and to
promote the health-related ▶ quality of life in
people confronted with a life-threatening disease.

Oncology

through all stages, depending on the respective
severity/progression of the disease. Before
actively seeking medical care, many patients are
confronted with early warning signs.

Early Warning Signs
Description
Oncology is the science that deals with cancer
diseases. Cancer represents a heterogeneous
subcategory of tumors with malignant growth.
“Cancer is not just one disease, but a large
group of almost 100 diseases. Its two main
characteristics are uncontrolled growth of the
cells in the human body and the ability of these
cells to migrate from the original site and spread
to distant sites. If the spread is not controlled,
cancer can result in death” (Medical Dictionary,
2011). In other words, cancer is a life-threatening
disease growing uncontrollably, directly
invading and destroying adjacent tissue, and
metastasizing to regional lymph nodes or distant
body sites.
Estimations of incidence and mortality data
for 25 cancers in 40 European countries in 2008
(Ferlay, Parkin, & Steliarova-Foucher, 2010)
result in 3.2 million new cases and 1.7 million
deaths. Worldwide, estimated data reveal about
12.7 million new cancer cases and 7.6 million
deaths in 2008 (Jemal et al., 2011). Overall,
cancer is a preventable disease, only 5–10 % of
cancer cases proved to be genetically determined,
whereas the majority (90–95 %) are supposed
to be determined by environmental and life style
factors (Anand et al., 2008).
Cancer has a fundamentally negative
impact on the lives of patients in terms of their
health-related ▶ quality of life (Boini, Briançon,
Guillemin, Galan, & Hercberg, 2004). In order to
better understand the severe psychological
situation they are in, five phases of cancer
disease or prognostic stage can be distinguished
(Fawzy, 1999; McCormick & Conley, 1995):
diagnosis, initial treatment, rehabilitation/recovery, recurrence and retreatment, and palliative/
terminal phase. However, not every patient goes

Before entering the medical system, first illness
symptoms can already impair people’s ▶ quality
of life harshly. The American Cancer Society
(2011) has identified five general signs and symptoms that might indicate a cancer disease. It is
strongly recommended to see a doctor if these
symptoms prevail over a long period of time
and get worse. These are:
• Unexplained weight loss
• Fever
• ▶ Fatigue
• ▶ Pain
• Skin changes
Additionally eight signs and symptoms
persons are suffering from might be indicants of
certain cancers:
• Change in bowel habits or bladder function
• Sores that do not heal
• White patches inside the mouth or white spots
on the tongue
• Unusual bleeding or discharge
• Thickening or lump in the breast or other parts
of the body
• Indigestion or trouble swallowing
• Nagging cough or hoarseness
Patients differ with respect to their attitudes
toward these signs and symptoms. Some people
repress their physical complaints (i.e., the
threat is ejected from consciousness) as long as
possible, so that they do not perceive the
symptoms anymore and are thus no longer
aware of the danger. Others might be
demoralized, fearing “the beginning of the end”
and emphasizing the hopelessness of the
situation, while others may break down psychologically, worrying about not to be able to cope
with the horrible situation. Finally, patients may
follow a realistic orientation and realize that
a cancer disease may be the cause, making use
of medical advice and care.

Oncology

Before consulting a medical doctor, individuals often seek advice from their lay referral
system, that is, they ask their family or members
of their community what to do about their
symptoms, or they contact paramedical suppliers.
On the contrary, many cancer diseases in their
early stage are symptom free. Undergoing
a medical examination, blood tests might reveal
telltale signs. Subsequent radiological imaging
(e.g., X-ray) or biopsy may yield a positive result.
The patient then enters the diagnosis phase.

Diagnosis
In the diagnosis stage, the patient is confronted
with a life-threatening diagnosis which changes
his or her life profoundly. From medical doctors’
perspective, breaking serious news feels like
dropping a bomb and is emotionally challenging.
Cancer specialists are not well trained to have
a look at the psychological issues; they focus on
treating the cancer. Oncologists can benefit
from communication guidelines that teach to
adequately
communicate
with
patients,
addressing both biological and psychosocial
issues in a patient-centered way (Back, Arnold, &
Tulsky, 2009; Baile et al., 2000). More than
90 % of patients want to hear the diagnosis.
Discussing prognostic concerns is also emotionally demanding, about 60 % of patients ask
such questions. Depending on the cancer type
and progression, the disease may be expected
to take a:
• Curative course, which means that the patient
will be definitely a cancer survivor
• Chronic course, which means that the patient
will oscillate between hope (to get the disease
under control) and frustration (having to cope
with relapses or metastasis), trying to lead
a “normal” life, if possible
• Rapid progredient/infaust/palliative course,
which means that healing is no more possible
From patients’ perspective, a cancer diagnosis
is a life-altering diagnosis. Life will never be the
same again. In many cases, this stressful life
event causes a severe shock for patients and
their relatives and often precipitates an existential

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crisis, feeling completely helpless in the face of
loss, suffering, dying, and death. The patient’s
involvement with existential topics becomes
especially more apparent in the later phases
(see below). Furthermore, the sense of physical
integrity, which is so self-evident for healthy
people, is fundamentally shattered; the body
does not function as it used to do. Cancer
is a psychologically annihilating disease (Block,
1997). At the outset, the patient has no coping
strategies at his or her disposal to adequately
respond to this peril. A very important
coping strategy proved to be problem analysis
and information seeking with respect to all
aspects of a given cancer disease.
Being told that one suffers from cancer creates
tremendous emotional distress and has detrimental effects on the patient’s ▶ emotional
well-being. The threat is real, and a normal
human response is anxiety. ▶ Anxiety is
a persistent psychophysiological state characterized by a number of symptoms such as autonomic
overactivity (e.g., palpitation, tremor, and other
physiological responses such as nausea, diarrhea,
pain), intrusive thoughts (e.g., worrying,
thoughts of recurrence of disease, ▶ disability,
sudden death) and concentration problems
(feeling empty in the head, difficulties in memorizing things), emotional responses (e.g., fear of
dying, tension, panic, restlessness, irritability),
and excessive behaviors (e.g., withdrawal, agitation, reassurance seeking).
In principle, ▶ anxiety is adaptive because it
motivates the individual to take actions and cope
with the threat. Conversely, showing only little
signs of ▶ anxiety can be problematic for managing the situation, and high levels of anxiety are
maladaptive because the individual is disrupted
from everyday functioning. In the case of excessive anxiety, cancer patients can develop an
▶ anxiety disorder. These are circumstances in
which the condition of anxiety is disproportionate
to the actual threat, which means that anxiety is
irrational or neurotic. However, those criteria
defining anxiety disorders such as phobic anxiety
disorder, generalized anxiety disorder, or panic
disorder are difficult to apply to cancer patients
because the threat of the stressor “cancer” is

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realistic. Another category, anxious adjustment
disorder, also does not fully apply because the
time criteria do not fit (onset within the first
month after the stressful event and duration no
more than 6 months).
Anxiety is common among cancer patients
(Stark & House, 2000; Stark et al., 2002). When
screening instruments are used, about every second, patient reported a high level suggesting an
anxiety disorder. However, the prevalence rate
proved to be smaller, yielding 18 % (Stark et al.,
2002). Especially intrusive thoughts prevail in
cancer patients which can be labeled as fear of
▶ health (Stark et al., 2004) or fear of cancer
progression (Lee-Jones, Humphris, Dixon, &
Hatcher, 1997; Mehnert, Berg, Henrich, &
Herschbach, 2009). Recently, cancer or cancer
treatment (see below) has been identified as
a life-threatening stressor. In psychotherapeutic
screenings, patients also manifest symptoms
defining the posttraumatic stress disorder
(PTSD), namely, intrusions, hyperarousal, and/
or cognitive avoidance. However, the majority
does not manifest all defining criteria
(Mehnert et al., 2009; Mehnert & Koch, 2007).
In addition to anxiety, depression can be
a response to a life-threatening diagnosis.
Depression is a mood disorder, typically
represented by three key symptoms (following
ICD-10 criteria): depressed mood (persistent sadness), anhedonia (loss of interests or pleasure),
and reduced energy (▶ fatigue). Further symptoms can be disturbed sleep, poor concentration
or indecisiveness, low ▶ self-confidence, poor
or increased appetite, suicidal thoughts or acts,
agitation or slowing of movements, and
self-blame. Similar to ▶ anxiety disorders, it is
also problematic to diagnose a depressive episode
because somatic symptoms can be largely
the outcome of disease processes (e.g., loss of
appetite and weight loss, ▶ fatigue, anhedonia).
This means that depression as a mental disorder
cannot be readily applied to cancer patients.
Empirical studies suggest that the prevalence of
depression among cancer patients and the
normal population does not differ (McDaniel,
Musselman, Porter, & Reed, 1995; Sellick &
Crooks, 1999), which largely means that cancer

Oncology

does not cause depression. However, people
living with cancer do suffer from depressive symptoms that negatively affect their ability to comply
with the complex and stressing treatment regimes.
Most importantly, a recent review revealed that
cancer patients hold an elevated risk for suicide
ideation and committed suicide (Robson, Scrutton,
Wilkinson, & MacLeod, 2010).
With respect to the treatment plan, medical
doctors should involve their patients in decision-making, if they wish to (Back et al., 2009).

Initial Treatment
Shortly after having received the serious diagnosis, the patient undergoes a series of initial stressful medical treatments which are experienced
as a fundamental crisis, because he or she is
completely at the mercy of these treatments and
their possibly severe side effects. The most
frequent treatment types are chemotherapy,
radiation therapy, and surgery, which are often
applied in combination.
Cancer cells normally grow and divide more
rapidly than normal cells. Chemotherapy aims at
killing these malignantly growing cells. However, healthy cells also multiply quickly, and
chemotherapy might kill these cells, too. This
damage to healthy cells determines the quality
and intensity of side effects. Typically, hair follicles are affected, but also blood cells forming in
the bone marrow as well as cells in the digestive
and reproductive systems or even cells of vital
organs are damaged. The list of side effects
which affect ▶ quality of life negatively is long.
Many patients experience physical problems
such as loss of their hair, nausea and vomiting,
▶ fatigue, diarrhea and constipation, urination
problems, ▶ Pain, infections, anemia and bleeding problems, or hurting of mouth and throat.
But also psychological problems can be overwhelming such as distress, worries, intrusive
thoughts, fear of ▶ health and death, despair,
sadness, ▶ anxiety, depression, PTSD, sexual
problems, confusion, or memory problems.
In many cases, patients feel emotionally imbalanced, like riding on a rollercoaster.

Oncology

Radiation therapy is also used to destroy
cancer cells. As in chemotherapy, the radiation
that destroys cancer cells also damages healthy
cells. This damage to healthy cells determines the
quality and intensity of side effects. Side effects
of radiation therapy are similar and cause similar
emotional problems.
On a psychological level, some types of
surgery fundamentally shatter a person’s body
image and ▶ self-concept. Patients might feel
disfigured. For example, women having undergone mastectomy (removal of the entire breast)
suffer from a loss of attractiveness/sexual identity, or persons having undergone colon surgery
with colostomy have problems in leading
a normal sexual life.
In this initial treatment phase, the physically
and emotionally weakened patient is spending his
or her time in an oncology clinic, missing work
days and being separated from his or her family
and friends for months. Being thrown out of his or
her normal life can lead to uncertainties with
respect to social roles and tasks. Within the clinic,
he or she has to adapt to the new situation, getting
acquainted with the loss of privacy, entering new
relationships with medical professionals, or
getting accustomed to daily hospital routines.

Rehabilitation/Recovery
If things work out well, the patient enters the
rehabilitation/recovery phase, having accomplished the arduous medical treatment.
Exhausting surgery, chemotherapy, and radiation
therapies are terminated. As follow-up examinations do not reveal any metastasis, the patient
may hope that the cancer disease has been
cured. The “normalization” of life begins,
although such “normality” does not exist
anymore. Thus, the patient has to rearrange his
or her life and personal relationships,
reintegrating the stressful experiences in his
or her autobiography, redefining one’s social
position, and setting priorities for the future. In
this phase, the patient is hoping to be a survivor,
although doubts and fears remain. It is important
to learn to sustain such uncertainties.

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The US National Cancer Institute (2011)
defines a cancer survivor “as anyone who has
been diagnosed with cancer, from the time of
diagnosis through the balance of his or her life.”
Bloom (2002) differentiates between three
stages of survivorship on an individual’s life trajectory. The first stage is called acute survival and
encompasses the time period from the diagnosis
through the first year, which generally includes
treatment (see above). The second stage is
called extended survival and comprises the
time period from 1 year after diagnosis until
3 years later, representing a time frame in
which the probability of cancer relapse is supposed to be highest. The third stage is called
permanent survivor and starts after 4 years after
first diagnosis. As Bloom (p. 90) summarizes
for the last stage, “general problems that continue to be reported are permanent loss
in energy and relationship issues. Issues such
as reintegration into society, resuming prior
activities including jobs and recreational activity and the late effects of therapy become prominent concerns.”
Recent research has focused on the existential
problems of cancer survivors, recognizing human
beings as spiritual beings (see below). Mehnert
and Vehling (2011) point out that loss of meaning, demoralization, and embitterment are central
psychological issues of cancer survivors. Frankl
(1997) postulates an inner will to meaning as
a strong human motive, which is central for successful coping with stressful situations. Many
cancer survivors are incapable of integrating
their liminal experiences in their autobiography
and in telling a coherent life story. Thus, the
traumatic experience of a cancer disease may
shatter an individual’s purpose in life, devastating premorbid representations of the self, life
attitudes, and values. On the other hand, cancer
patients also report positive changes after
going through traumatic experiences, testifying
▶ personal growth. Those positive changes
referred to personal relationships, personal
strengths, self-understanding, ▶ spirituality,
new possibilities, or appreciation of life.
Refinding meaning in one’s life seems to be helpful to overcome these stressful experiences

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Oncology

(Barskova & Oesterreich, 2009; Morris, Campbell, Dwyer, Dunn, & Chambers, 2011; Park,
Chmielewski, & Blank, 2010).
Demoralization represents a syndrome of
existential distress and despair, which should be
differentiated from depression (Clarke &
Kissane, 2002). It is characterized by subjectively
“unique” feelings of helplessness/hopelessness,
feelings of being unable to cope, loss of sense of
purpose, feelings of pointlessness, loneliness, and
feeling completely alienated. Embitterment represents another reaction as a consequence of
a life-threatening disease, which is experienced
as unjust, as an insult, or as a humiliation
(Linden, Baumann, Rotter, & Schippan, 2008).
The posttraumatic embitterment disorder is
characterized by feelings of embitterment and
resignation, helplessness, heightened arousal,
rage, restlessness, repeated intrusive thoughts,
negative mood, and irritability.

Palliative/Terminal Phase

Recurrence and Retreatment

It is important not to equate palliative
care with the terminal phase (death, dying, and
hospice). Cancer patients may live in a palliative
phase for years without being in an end-of-life
situation. However, depending on the cancer progression, the palliative and terminal phase can
overlap to a large degree.
According to this definition of palliative care,
success of therapeutic interventions is given
if the patient’s idiosyncratic quality of life is
improved or stabilized and the patient is able to
live as autonomously as possible. In contrast
to the traditional biomedical thinking, which
emphasizes objective criteria such as decrease
in morbidity or mortality, the palliative paradigm
emphasizes the patient’s subjective perspective,
his or her reality. As the cancer cannot be cured
anymore, the main objective is symptom control
prevention and alleviation of suffering. The
WHO concept of palliative care understands the
patient as a “holistic” being and proposes
a “biopsychosocial-spiritual model of man”
(Sulmasy, 2002).
Spirituality refers to an individual’s relationship with the transcendent; it is “about the search
for transcendent meaning” (Sulmasy, 2002, p. 25),

If the initial treatment could not defeat the cancer
completely, or if after initial successful treatment
the cancer comes back, the patient enters the
recurrence and retreatment phase. It becomes
obvious that the cancer disease is taking
a chronic course. Especially recurrence of the
disease is psychologically the maximum disaster,
attacking patients’ autonomy and dignity. On an
emotional level, patients frequently experience
hopelessness, depression and anhedonia, anxiety
and panic, and/or guilt.
In essence, the ultimate therapy goal remains:
cure of the cancer disease (anticancer therapy).
The patient reenters the taxing treatment phase.
In principal, this is a re-traumatizing experience
to undergo chemotherapy or radiation therapy
with all their possible side effects again. In most
cases, patients have to learn living with long-term
side effects such as ▶ fatigue, nausea and
vomiting, and ▶ Pain. Recurrence of cancer
causes and enforces depression and suicidality,
▶ anxiety, loss of meaning, demoralization,
posttraumatic stress and embitterment disorder
(see above) (Mehnert & Vehling, 2011).

When medical doctors together with patients
and their family members realize that they
have no further anticancer options, which
would not be able to prolong the patient’s
life but would have unacceptable effects on
his or her quality of life, the goals of medical
care should be changed. This transition from
a curative to a palliative treatment is a very
incriminating for both medical doctors and
persons concerned. The World Health Organization (WHO) proposes the following definition of palliative care:
Palliative care is an approach that improves the
quality of life of patients and their families facing
the problem associated with life-threatening illness, through the prevention and relief of suffering
by means of early identification and impeccable
assessment and treatment of pain and other problems, physical, psychosocial and spiritual. (WHO,
2011)

Oncology

which must not be equated with religion. While
some find meaning in a superior being such as
God, others express their spirituality in their
relationships with nature, music, art, philosophy, or in their relationships with their family
and friends. The idea of the human being as
a spiritual being implies the notion that
the human being is a being in relationship.
Sulmasy differentiates between intrapersonal
and extrapersonal relationships. Whereas the
former imply physical (intrasomatic) relationships and mind-body (psychosomatic) relationships, the latter denote relationships with the
physical environment, social environment, and
the transcendent. Cancer as a life-threatening
disease disturbs relationships on all these levels
of human existence, not only on the biological,
somatic level. Palliative care sensu the
WHO definition follows a holistic approach to
healing, which “means the restoration of right
relationships. (. . .) [It] is a system of health care
that attends to all of the disturbed relationships
of the ill person as a whole, restoring those that
can be restored, even if the person is not thereby
completely restored to perfect wholeness”
(Sulmasy, p. 26).
Especially in the end-of-life situation, spiritual
questions concerning the worth of one’s life,
meaning of one’s life, and the meaning of interpersonal relationships arise. Such questions
prompt answers concerning one’s dignity, hope,
and forgiveness, respectively. “To die believing
that one’s life and death have been of no value is
the ultimate indignity. To die believing that there
is no meaning to life, suffering, or death is abject
hopelessness. To die alone and unforgiven is utter
alienation” (Sulmasy, 2002, p. 26).

Cross-References
▶ Anxiety
▶ Cancer Survivor(s)
▶ Disability
▶ Emotional Well-Being
▶ Fatigue
▶ Health
▶ Meaning in Life

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▶ Pain
▶ Patient-Physician Communication
▶ Personal Growth
▶ Self-Concept
▶ Self-Confidence
▶ Sleep, an Overview
▶ Social Integration
▶ Spirituality

References
American Cancer Society (2011). Learn about
cancer. Retrieved November 7, 2011, from http://
www.cancer.org/Cancer/CancerBasics/signs-and-sym
ptoms-of-cancer
Anand, P., Kunnumakkara, A. B., Sundaram, C.,
Harikumar, K. B., Tharakan, S. T., Lai, O. S., et al.
(2008). Cancer is a preventable disease that requires
major lifestyle changes. Pharmaceutical Research,
25(9), 2097–2116.
Back, A. L., Arnold, R. M., & Tulsky, J. A. (2009).
Mastering communication with seriously ill patients.
Balancing honesty with empathy and hope.
Cambridge, England: Cambridge University Press.
Baile, W. F., Buckman, R., Lenzi, R., Glober, G., Beale,
E. A., & Kudelka, A. P. (2000). SPIKES-A six-step
protocol for delivering bad news: A pplication to the
patient with cancer. The Oncologist, 5(4), 302–311.
Barskova, T., & Oesterreich, R. (2009). Post-traumatic
growth in people living with a serious medical condition and its relations to physical and mental health:
A systematic review. Disability and Rehabilitation: An
International, Multidisciplinary Journal, 31(21),
1709–1733.
Block, K. I. (1997). The role of the self in healthy cancer
survivorship: A view from the front lines of treating
cancer. Journal of Mind-Body Health, 13, 6–23.
Bloom, J. R. (2002). Surviving and thriving? PsychoOncology, 11(2), 89–92.
Boini, S., Briançon, S., Guillemin, F., Galan, P., &
Hercberg, S. (2004). Impact of cancer occurrence on
health-related quality-of-life: A longitudinal pre-post
assessment. Health and Quality-of-Life Outcomes,
2. doi:10.1186/1477-7525-1182-1184.
Clarke, D. M., & Kissane, D. W. (2002). Demoralization:
Its phenomenology and importance. The Australian
and New Zealand Journal of Psychiatry,
36(6), 733–742.
Fawzy, F. I. (1999). Psychosocial interventions for
patients with cancer: What works and what doesn’t.
European Journal of Cancer, 35(11), 1559–1564.
Ferlay, J., Parkin, D. M., & Steliarova-Foucher, E. (2010).
Estimates of cancer incidence and mortality in Europe
in 2008. European Journal of Cancer, 46(4), 765–781.
Frankl, V. E. (1997). Man’s search for ultimate meaning.
New York: Insight Books/Plenum Press.

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Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., &
Forman, D. (2011). Global cancer statistics.
CA: A Cancer Journal for Clinicians, 61(2), 69–90.
Lee-Jones, C., Humphris, G., Dixon, R., & Hatcher, M. B.
(1997). Fear of cancer recurrence-a literature review
and proposed cognitive formulation to explain exacerbation of recurrent fears. Psycho-Oncology,
6(2), 95–105.
Linden, M., Baumann, K., Rotter, M., & Schippan, B.
(2008). Diagnostic criteria and the standardized
diagnostic interview for posttraumatic embitterment
disorder (PTED). International Journal of Psychiatry
in Clinical Practice, 12(2), 93–96.
McCormick, T. R., & Conley, B. (1995). Patient
perspectives on dying and on the care of dying patients.
The Western Journal of Medicine, 163, 236–243.
McDaniel, J. S., Musselman, D. L., Porter, M. R., & Reed,
D. A. (1995). Depression in patients with cancer:
Diagnosis, biology, and treatment. Archives of
General Psychiatry, 52(2), 89–99.
Medical Dictionary (2011). Cancer. Retrieved
November 4, 2011, from http://medical-dictionary.
thefreedictionary.com/Cancer + %28disease%29
Mehnert, A., Berg, P., Henrich, G., & Herschbach, P.
(2009). Fear of cancer progression and cancer-related
intrusive cognitions in breast cancer survivors.
Psycho-Oncology, 18(12), 1273–1280.
Mehnert, A., & Koch, U. (2007). Prevalence of acute and
post-traumatic stress disorder and comorbid mental
disorders in breast cancer patients during primary
cancer care: A prospective study. Psycho-Oncology,
16(3), 181–188.
Mehnert, A., & Vehling, S. (2011). Cancer patients: Loss of
meaning, demoralization and embitterment. In
M. Linden & A. Maercker (Eds.), Embitterment: Societal, psychological, and clinical perspectives
(pp. 142–153). New York: Springer-Verlag Publishing.
Morris, B. A., Campbell, M., Dwyer, M., Dunn, J., &
Chambers, S. K. (2011). Survivor identity and
post-traumatic growth after participating in
challenge-based peer-support programmes. British
Journal of Health Psychology, 16(3), 660–674.
National Cancer Institute (2011). US cancer survivors
grows to nearly 12 million. Retrieved November
7, 2011, from http://www.cancer.gov/newscenter/
pressreleases/2011/survivorshipMMWR2011
Park, C. L., Chmielewski, J., & Blank, T. O. (2010).
Post-traumatic growth: Finding positive meaning in
cancer survivorship moderates the impact of
intrusive thoughts on adjustment in younger adults.
Psycho-Oncology, 19(11), 1139–1147.
Robson, A., Scrutton, F., Wilkinson, L., & MacLeod, F.
(2010). The risk of suicide in cancer patients: A review
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Sellick, S. M., & Crooks, D. L. (1999). Depression and
cancer: An appraisal of the literature for prevalence,
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One-Night Stands
Stark, D., & House, A. (2000). Anxiety in cancer patients.
British Journal of Cancer, 83(10), 1261–1267.
Stark, D., Kiely, M., Smith, A., Morley, S., Selby, P., &
House, A. (2004). Reassurance and the anxious cancer
patient. British Journal of Cancer, 91(5), 893–899.
Stark, D., Kiely, M., Smith, A., Velikova, G., House, A., &
Selby, P. (2002). Anxiety disorders in cancer
patients: their nature, associations, and relation to
quality-of-life. Journal of Clinical Oncology,
20(14), 3137–3148.
Sulmasy, D. P. (2002). A biopsychosocial-spiritual model
for the care of patients at the end of life. Gerontologist,
42(SpecIssue3), 24–33.
WHO (2011). WHO definition of palliative care.
Retrieved November 7, 2011, from http://www.who.
int/cancer/palliative/definition/en/

One-Night Stands
▶ Casual Sex and the Quality of Life

One-Parent Families (UK)
▶ Single-Parent Families

One-Time Sexual Encounters
▶ Casual Sex and the Quality of Life

Online Panels
▶ Web-Based Questionnaire

Online Polling
▶ Web-Based Questionnaire

Online Survey
▶ Web-Based Questionnaire

Opportunity Cost

Open Government
▶ Community Participation

Open Question Argument

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financial cost, for it refers to any real cost in terms
of things forgone (time, output, money. . .) and
everything that can provide us with a certain level
of utility (Frank, 2005; Gold et al., 1996). The
concept of utility is crucial to fully understand the
meaning of opportunity costs (Buchanan, 2008;
Henderson, 2008; Pearce, 1992).
We can put it in this way: “there’s no such
thing as a free lunch.”

▶ Fact/Value Dichotomy

Description

Operationalization
▶ Questionnaire Design

Opinion of European Citizens on
Public Services
▶ Europeans’ Opinions on Services

Opportunity Cost
Carme Riera-Prunera
Econometrics, Statistics and Spanish Economy,
University of Barcelona, Barcelona, Spain

Synonyms
Economic opportunity loss; Implicit cost

Definition
Generally speaking, the opportunity cost of doing
something is the cost of doing it measured in
terms of an alternative thing or action, the best
alternative forgone, that is to say, what man could
have gained when making another choice. Put it
into economic terms, it is the risk of achieving
greater benefits had you taken a different option.
But it should not be restricted to a monetary or

The concept of opportunity cost was first developed by Professor Friedrich von Wieser (1914),
a member of the Austrian School of Economics
who exercised a strong influence on economists
such as von Mises, Hayeck, or Schumpeter,
the next generation of Austrian economists. The
concept appeared in his book “Theory of social
Economics,” published in 1914 with the intention
to apply his ideas to the real world. In this line, he
became Austrian finance minister in 1917.
The opportunity cost could be interpreted as
the true cost of something, if we consider that cost
as what we give up in order to get the thing we
want. In fact, when saying the cost of something,
we include not only the money spent buying (or
doing) something but also the utility it provides
with respect to the alternative; that’s the benefits
(economic or other) we will not be able to get
with another thing (option) we did not buy
(made). For example, the opportunity cost of
being an engineer (Stigler, 1955) is not merely
the money spent on tuition fees, books, and so on
but also the fact that you are no longer able to
spend time carrying out a job or becoming a popstar. These alternatives could highly contribute to
increase your utility, and you might be getting
less utility because of your choice. But you are
not able to know it beforehand. In any case, when
we are considering the opportunity costs of decisions we make, we must use the highest-valued
alternative that has had to be sacrificed
for the option we have chosen (McConnell
et al., 2012).
It is sometimes known also as the economic
opportunity loss. In this sense, it is a way of

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measuring the cost of a decision in terms of the
likely decision’s alternative. Therefore, it is
a way of working out the cost of not taking
the next best alternative (Baye, 2007; Dawson,
1994). Consequently, it is not simply a number,
but it goes further: it measures the benefits of the
next best alternative to the action you did (Posnett
& Jan, 1996; Tucker, 2006). But other factors, not
just financial, can also be taken into account. We
could include the pleasure of making one
decision against the other, business ethics or
other various social factors.
The crucial point is the existence of a choice,
which has to be made between different options
without knowing the final result. In fact, people’s
decision and choices about the goods and services
they are to consume determine how economic
resources will be used. This introduces the
concept of “risk” associated to any decision we
take. The opportunity cost of going for a walk
could be measured, for instance, as the opportunity forgone to read a book and having a part-time
job. In any case, everything we do has an associated opportunity cost, either in monetary terms or
in terms of time. And since both money and time
are scarce resources, this translates into the fact
that the notion of opportunity cost has to do
mainly with the efficient use of scarce resources.
If we were to live in an economy with no scarce
resources, then no things or actions would be
bought or undertaken at the expense of any
other; thus, in this case, the opportunity cost
would be clearly zero. But we live in a world
where all available resources are limited, and by
minimizing the opportunity costs of doing or
buying something, we are somehow ensuring an
efficient use of resources (using them for a certain
purpose prevents their use for any other). In
economics, it is crucial to describe the relationship between scarcity and choice. This is one of
the main reasons why opportunity cost is a key
concept in economics since it implies the choice
between mutually exclusive results. Furthermore,
the choices have both present and future consequences for the whole economy. Thus, the
concept of opportunity cost allows economists
to examine the relative monetary values of
goods and services in the whole economy.

Opportunity Cost

In terms of investing, the opportunity cost is
the difference in return between two investments,
the one you made, and another one you could
have made, but did not. In this case, the opportunity cost is the money you gave up due to your
choice in favor of an investment that resulted in
lower profits. For instance, if you have 20.000 m.u.
and doubt between investing in the stock market
and putting it in a savings account, when choosing
the second option because you are highly
risk-averse, the opportunity cost would be the
potential of making more money from buying
shares than the regular bank interest rate.
Hence, opportunity cost is an important
economic principle that affects the value of our
financial decisions.
How to Calculate Opportunity Cost
in Economics
The easiest and more intuitive way to calculate
the opportunity cost would be the next one. First
you need to outline the monetary value of all of
your options. Second, you must determine the
value of the option you decide to choose. Third,
determine the value of the next best alternative.
Finally, subtract the value of the next best
alternative from the value of the option you select
to find the opportunity costs. You must be aware
that you only subtract the cost of the next best
alternative, not all alternatives not taken. A useful
rule of thumb for including opportunity cost in
our financial life could be the next one. Imagine
you face the following trade-off, either pay off
your debt or invest some money you have. If you
decide to pay off your debt instead of investing
the money, subtract the money you could have
earned on the investment to calculate the net
savings. On the contrary, if you decide to invest,
subtract the interest you could have paid off
on the debt.
However in order to be strict and accurate, we
have to be attentive to how costs are measured
since this is another choice influencing the
results. Ideally, a certain use should be compared
with all relevant uses, including doing nothing.
Failure to choose a correct comparison may lead
to incorrect results with respect to the amount of
estimated costs and the effectiveness of each

Opportunity Cost

option involved. In this sense, a correct measure
of opportunity costs would require methods to
derive what economists call “shadow prices” –
the true social value of an action or object. Moreover there are some difficulties in applying the
concept of opportunity costs (Russell, 1992),
which arises from the fact that the calculations
are not always accurate. This is because the costs
are often based on estimates. Consequently,
despite the importance of this concept, complexities of its application translate into the fact that
few studies are completely explicit about their
estimates of opportunity costs.
Things vary when thinking in terms of accountancy. Opportunity costs are not written on any
financial statements or accounts, like we do with,
for example, depreciation. Since every business
choice has an opportunity cost, all the alternatives
should be considered before taking a crucial
business decision, which is absolutely impossible
(Torgerson & Spencer, 1996; Baye, 2007). That
might be the reason why, in fact, accounting
practices do not aim to measure opportunity
costs. In fact, accountancy and economics do
not agree on their definitions of cost. Accountancy focuses on the money outlays on the various resources required to produce a product,
whereas economists may consider prices as inaccurate to fully reflect the opportunity costs of
resources. This is so because they take into
account the possibility that markets might fail
when considering the value of those resources in
their highest value alternative use (Stiglitz,
1986). In short, economists are concerned not
only about the private opportunity cost but also
about the social opportunity cost, hence about all
the benefits sacrificed by taking an action.
Needless to say that the perspective of the
analysis is also crucial since it will determine
which costs should be included in the analysis.
For example, from a social perspective, we
should include all the costs and benefits regardless of who incurs or obtains them, whereas from
an individual perspective (firm, people. . .), the
analysis can be more restrictive, including only
the costs and benefits concerning each part
(Wittcoff et al., 2004). Going into more depth,
opportunity cost can also be calculated through

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cost-effectiveness or cost utility studies. This
analysis makes the opportunity cost of the alternative uses of resources explicit and thus comparable, when two or more alternatives are
analyzed. Recent studies seem to indicate that
for a correct estimation, the incremental
cost-effectiveness ratio should also be estimated
in order not to mislead the decision makers.
Finally, since economic concepts can be better
understood when posing examples containing
the concept we want to explain, the article
“Baseball Players and Opportunity Costs”
provides you a good example illustrating how
opportunity costs can be used in practice.

Cross-References
▶ Cost-Benefit Analysis

References
Baye, M. R. (2007). Managerial economics and business
strategy (6th ed.). New York: McGraw Hill.
Buchanan, J. M. (2008). Opportunity cost. In:
S. N. Durlauf & L. E. Blume (Eds.), The new Palgrave
dictionary of economics (2nd ed.). Palgrave Macmillan.
The New Palgrave Dictionary of Economics Online.
Palgrave Macmillan. 09 June 2013 http://www.dictionaryofeconomics.com/article?id=pde2008_O000029.
doi:10.1057/9780230226203.1222
Dawson, D. (1994). Costs and prices in the internal
market: Markets versus the NHS management executive guidelines. York: Centre for Health Economics,
University of York.
Frank, R. H. (2005). The opportunity cost of economics
education. New York Times.
Gold, M. R., Siegel, J. E., Russell, LB., & Weinstein,
M. C. (1996). Cost-effectiveness in health and
medicine. New York: Oxford University Press.
Henderson, D. R. (2008). Opportunity cost. In Henderson,
D. R. (Eds.), The concise encyclopedia of economics
(pp. 385). Indianapolis, IN: Liberty Fund.
McConnell, C., Brue, S. L., & Flynn, S. M. (2012). Microeconomics: Principles, problems, and policies (p. 12,
123) (9th ed.). New York: McGraw-Hill Irwin.
Pearce, D. W. (Ed.). (1992). The MIT dictionary of modern
economics (4th ed.). Cambridge, MA: The MIT Press.
Posnett, J., & Jan, S. (1996). Indirect cost in economic
evaluation: The opportunity cost of unpaid inputs.
Health Economics, 5, 13–23.
Russell, L. B. (1992). Opportunity costs in modern
medicine. Health Affairs, 11, 162–169.

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Stigler, G. J. (1955). The nature and role of originality in
scientific progress. Economica, XXII (88), 293–302.
Stiglitz, J. E. (1986). Economics of the public sector.
New York: Norton.
Torgerson, D. J., & Spencer, A. (1996). Marginal costs
and benefits. British Medical Journal, 307, 726–728.
Tucker, I. B. (2006). Survey of economics (5th ed.). Ohio:
Thompson.
von Wieser, F. (1914). Theorie der gesellschaftlichen
Wirtschaft. English translation Social Economics
(1927), translated into English by A. Ford Hinrichs,
with a preface by Wesley Clair Mitchell. New York:
Adelphi and co. Reprinted by Augustus M. Kelly,
New York, (1967).
Wittcoff, H. A., Reuben, B. G., & Plotkin, J. S. (2004).
Industrial organic chemicals. Hoboken, NJ: Wiley.

Oppressed Populations

Optimal Sleep
▶ Sleep and Well-Being

Optimism
Charles S. Carver
Department of Psychology, University of Miami,
Coral Gables, FL, USA

Definition

Oppressed Populations
▶ Marginalized Communities

Optimism (vs. pessimism) is a dimension of
personality defined by the expectation that good
(vs. bad) things will happen to oneself. Generalized optimism pertains to the person’s entire life,
rather than to just one domain or another.

Optimal Choice Theory
▶ Rational Choice Theory

Optimal Experience
▶ Flow, the Experience of

Optimal Functioning
▶ Eudaimonic and Hedonic Happiness

Optimal Sexual Intimacy
▶ Components of Optimal Sexual Experiences
▶ Lessons About Optimal Sexual Experiences
from Remarkable Lovers
▶ Personal Contributions to Optimal Sexual
Experiences
▶ Relational Contributions to Optimal Sexual
Experiences

Description
Optimists expect good things to happen to them;
pessimists expect bad things to happen to them.
Optimists and pessimists differ in how they
approach problems and the success with which
they cope with adversity. These differences have
important implications for their psychological
and physical quality of life.
Defining optimism and pessimism in terms of
expectancies creates a link to broad expectancyincentive theories of motivation. Such theories
assume that behavior occurs in order to attain
desired goals. Engagement of effort depends on
having a goal that matters enough to try to reach
it, but it also depends on expectancy: confidence
versus doubt that the goal can be obtained. If
people lack confidence, there is no action. If the
person loses confidence along the way, action
will stop. Only when confidence is high enough
do people undertake and remain engaged in
goal-directed efforts (Carver & Scheier, 2009;
Scheier & Carver, 2009).
Expectancies exist at many levels of generality.
An elderly person can have an expectancy about

Optimism

being able to move her fingers enough to button
her coat, an expectancy about being able to drive
to get her hair cut, and an expectancy about living
a fulfilling life for several more years. Optimism
and pessimism are the most general kinds
of expectancies. They are broad versions of confidence or doubt, pertaining to most situations in life
rather than just one or two. The principles in
expectancy-incentive theories should pertain
equally well to specific expectancies and generalized expectancies.
Measuring Optimism
Research on optimism has flourished over nearly
three decades. Several routes have been taken to
assessing optimism, yielding distinct literatures.
The simplest and most direct approach is to ask
people whether they think their outcomes in life
will be good or bad, for example, by having them
respond to statements such as I’m optimistic
about my future. Another approach relies on the
idea that people’s expectancies for the future
stem from their interpretations of the past.
Explaining bad outcomes by causes that persist
into the future and affect many kinds of events
implies pessimism. Such explanations imply that
bad outcomes will continue in the future. An
attributional style that explains negative events
by causes that are more time limited and narrower
in their effects implies more optimism.
Researchers on optimism often refer to
optimists and pessimists as though they were
categories, but all measurements of optimism
actually vary continuously across large numbers
of people. People range from very optimistic to
very pessimistic, with most falling somewhere
between.
Psychological Quality of Life
Many studies have examined relationships
between optimism and quality of life among
people undergoing adversity. The kinds of adversity have been extremely varied. They
include caregiving for patients suffering from
Alzheimer’s disease and having medical
procedures such as coronary artery bypass surgery, attempts at in vitro fertilization, and
heart and bone marrow transplantation. Yet

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other studies have looked at how people deal
with a diagnosis of cancer, the pain of arthritis,
and the progression of acquired immune deficiency syndrome.
The studies consistently find that optimists
experience less distress during times of adversity
than do pessimists (Carver & Scheier, 2009;
Carver, Scheier, & Segerstrom, 2010; Segerstorm,
2006). This holds for cross-sectional studies
and for prospective studies – that is, in which
optimism and distress were assessed at some baseline with distress reassessed later. The results of
the prospective studies are particularly important,
as they suggest that optimism predicts
better trajectories of quality of life over time.
Optimism and Coping
If optimists experience less distress than pessimists under adversity, is it just because optimists
are cheerful? No. If so, optimism would not
predict differential change in distress over time,
shown by prospective studies. There must be
other explanations. One answer is that optimists
and pessimists differ in the manner in which they
address adversity. In many ways, this difference
reflects the behavioral tendencies discussed
earlier. That is, people who are confident continue trying, even when it is hard. People who
are doubtful try to escape by wishful thinking
and temporary distractions that do not solve the
problem. They sometimes even stop trying.
Such differences in coping have emerged in
many studies. These studies typically examine
patient populations or people undergoing medical
procedures. These projects find that optimists
differ from pessimists in terms of their situational
coping reactions and their general, more stable
coping styles.
Optimists tend to seek information about the
problem they are facing and take problemfocused action when possible. They use humor
and reframe the situation as best they can
while accepting the reality that the stressor is
a challenge to them. Pessimists tend to try to
push the problem away rather than accepting
and dealing with it, become mired in their distress, and use cognitive and behavioral avoidance
as much as possible.

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A number of prospective studies have
contained measures of coping reactions in addition to measures of well-being. This allows
researchers to test whether differences in wellbeing were mediated by differences in coping.
This research has consistently shown that
psychological well-being and coping responses
are linked. Thus, optimists have better psychological outcomes than pessimists partly because
of differences in how they cope.
Physical Quality of Life
Much more is known about effects of optimism
on psychological well-being than on physical
well-being, but there is some evidence of links
to physical health and physiological functioning.
The findings from this research mirror those just
described: optimists typically experience better
outcomes – signs of better physical health or
signs of more adaptive physiological responses
when under adversity.
Compared to pessimists, optimists report
fewer physical symptoms during times of duress
and have higher health status across their lives.
Optimists are less likely to suffer negative side
effects from major surgery or to be rehospitalized
shortly after major surgery. They are also more
likely to benefit longer from their surgery. Less
pessimistic persons have been found to outlive
more pessimistic persons when diagnosed with
life-threatening illness. They also exhibit less
extreme cardiovascular reactivity during their
daily lives. Optimists tend to show signs of more
adaptive immune functioning than do pessimists,
but the evidence on this point is a good deal more
mixed than other physical health outcomes.
Health-Promoting and Health-Damaging
Behavior
As described, research documents that optimists
and pessimists cope differently with stress and
that these differences partly cause differences in
psychological outcomes. Coping differences may
also be partly responsible for physical health
differences as well. Studies show that optimists
face health threats head on and do whatever they
can to improve the situation. This seems to reflect
problem-focused coping, an attempt to engage in

Optimism

proactive processes that promote good health and
well-being.
For example, following coronary artery
bypass graft surgery, optimistic patients were
more likely than pessimists to enroll in cardiac
rehabilitation programs and to take vitamins and
eat low-fat foods. Optimistic patients are also
more likely to benefit from cardiac rehabilitation
than are pessimistic patients. Optimists are also
more likely than pessimists to seek out information about risk factors for major diseases.
Optimists take action to minimize health risks.
They do not simply stick their heads in the sand
and ignore threats to well-being. They attend to
risks but do so selectively. They focus on risks
that apply to them and relate to potentially serious
health problems. If the health problem is minor or
if it is unlikely to bear on them, they show no
special vigilance. Optimists appear to scan their
surroundings for threats to well-being but save
their behavioral responses for threats that are
truly meaningful.
If optimists face health threats head on and do
as much as they can to make themselves less
vulnerable, pessimists do the opposite. Pessimists
tend to give up when confronting problems in
life. Giving up can become manifest in ways
with negative health consequences. Giving up
may underlie various forms of substance abuse,
which is often seen as an escape from problems.
Pessimists are more vulnerable than optimists to
such maladaptive behavior.
Is Optimism Always Better than Pessimism?
Much evidence suggests optimists are better off
than pessimists. They are less distressed when
times are tough, they cope in ways that foster
better outcomes, and they are better at taking
the steps necessary to ensure that their futures
are bright. Although there are probably situations
in which optimists have no advantage, there is
remarkably little evidence that optimists are ever
worse off than pessimists.
Some have suggested that such situations
do exist, that optimism may be potentially
damaging. The logic is this: too much optimism
might lead people to ignore a threat until it is
too late or might lead people to overestimate

Optimism-Pessimism, Dispositional

their ability to deal with it, resulting in poorer
outcomes. This appears to be generally not the
case. However, occasional studies do suggest
adverse effects of optimism. For example, there
is some evidence that optimism predicts poorer
immune response under relatively high challenge
and that the buffering effect of optimism reverses
when life stress accumulates over time. Findings
such as these suggest that some caution is
warranted in concluding that optimism is invariably beneficial.

References
Carver, C. S., & Scheier, M. F. (2009). Optimism. In M. R.
Leary & R. H. Hoyle (Eds.), Handbook of individual
differences in social behavior (pp. 330–342).
New York: Guilford.
Carver, C. S., Scheier, M. F., & Segerstrom, S. C. (2010).
Optimism. Clinical Psychology Review, 30, 879–889.
Scheier, M. F., & Carver, C. S. (2009). Optimism. In
S. Lopez (Ed.), Encyclopedia of positive psychology
(Vol. 2, pp. 656–663). Oxford, UK: Wiley-Blackwell.
Segerstrom, S. C. (2006). Breaking Murphy’s law.
New York: Guilford.

Optimism-Pessimism, Dispositional
Daiva Daukantaite
Department of Psychology, Lund University,
Lund, Sweden

Definition
Dispositional optimism vs. pessimism have been
defined as a generalized tendency to expect
positive vs. negative outcomes in a person’s life
(Scheier & Carver, 1985).

Description
The theoretical principles that underlie the concepts of dispositional optimism and pessimism
are taken from expectancy-value models of
motivation, in which the initial assumption is

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that behavior is organized around the pursuit of
goals (Carver & Scheier, 2002). In this approach,
goals provide the value element – the more
important the goals are to people, the greater the
motivation is to move towards them. Expectancy,
“a sense of confidence or doubt about goal’s
attainability (or the anti-goal’s avoidability)”
(Carver & Scheier, 2002, p. 32), is another
element in this approach. Since optimists tend
to expect their actions will lead to a positive
outcome, they persist in those actions and achieve
their goals more often than pessimists who tend to
expect they will fail and often withdraw their
efforts and disengage from goals they have set.

Assessing Dispositional Optimism and
Pessimism
Scheier and Carver (1985) developed a measure
called the Life Orientation Test (LOT) to assess
differences between people in dispositional
optimism and pessimism. This way of measuring
involves asking people directly whether they
expect outcomes in their lives to be good or bad.
A briefer form, the Life Orientation Test-Revised
(LOT-R; Scheier, Carver, & Bridges, 1994)
containing six coded items and four filler items,
is used most often. Three of the six coded
items are positively worded (e.g., I am always
optimistic about my future), and three are
negatively worded (e.g., I hardly ever expect
things to go my way). According to the authors
of the scale, both the LOT and LOT-R provide
a continuous distribution of scores. For verbal
convenience, people are often called optimists or
pessimists; however, the majority of them cannot
be identified as optimists or pessimists in an absolute sense and rather they tend to fall on a range
from being very optimistic to very pessimistic,
with most people somewhere in-between.
The LOT-R scale is sometimes separated
into two factors: an optimism factor, containing
positively worded items, and a pessimism factor,
containing negatively worded items. However,
results and discussions are inconsistent
regarding whether the optimism construct should
be seen as a bipolar dimension or whether

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4504

there are two separate factors. Some studies (e.g.,
Robinson-Whelen, Kim, MacCallum, & KiecoltGlaser, 1997) have found that one subscale is
more important than the other in the prediction
of relevant outcomes; however, which subscale is
more important varies from study to study. Carver, Scheier, and Segerstrom (2010) pointed out
that this issue is still questionable, and some important clinical and theoretical questions – such as, in
what cases do the separation into two factors lead
to better predictions and should interventions be
targeted to separately address optimistic and pessimistic cognition in clinical settings – remain to be
answered. Carver and Scheier recommend examining the factors separately and reporting results
separately for the subscales only when the two
subsets of items behave differently.
The LOT-R has good internal reliability with
an average Cronbach’s alpha around 0.80 for the
total scale. Test-retest correlations are relatively
high, ranging from 0.58 to 0.79 for periods of
a few weeks to 3 years (Lucas, Diener, & Suh,
1996; Scheier & Carver, 1985; Scheier et al.,
1994). Long-term test-retest correlations have
varied from 0.35 (Segerstrom, 2007) to 0.71
(Matthews, R€aikkönen, Sutton-Tyrrell, & Kuller,
2004) across 10-year period.

Dispositional Optimism and Pessimism
and Mental Health
Much research has been conducted to investigate
the importance of optimism for mental health. It
has been shown that optimism is highly important
for subjective well-being (e.g., Marshall,
Wortman, Kusulas, Hervig, & Vickers, 1992;
Scheier et al., 1994; Daukantaite & Bergman,
2005). Findings indicate that more optimistic
people tend to report a higher level of life satisfaction and positive affect and a lower level of
negative affect. The strong direct relationships
between adult optimism and positive affect
or negative affect found in many studies are
consistent with the Carver and Scheier’s (2002)
proposition that high expectations for future
outcomes coincide with positive feelings and
doubtful expectations for future outcomes

Optimism-Pessimism, Dispositional

coincide with negative feelings. Although optimists tend to experience more positive emotions
and less negative emotions, this is only to a lesser
extent responsible for the strong relationship
between optimism and global life satisfaction
(Chang & Sanna, 2001; Daukantaite & Bergman,
2005). The results are consistent with the
proposition that a person who has a generalized
expectancy of good outcomes in life also tends to
have “chronic” positive expectancies which,
to a large extent, are used when evaluating life
as a whole (Schimmack, Diener, & Oishi, 2002).
Optimism also appears to be an important
resource for good social relationships. Optimistic
people have more friends and larger social
networks, report more satisfaction with relationships and better relationship quality, and are more
supportive partners as compared to pessimistic
people (for a review, see Carver et al., 2010).
Some possible explanations for these findings
are that optimists in general seem to be
more liked than pessimists because of their
positive expectations for the future, their ability
to see things in the best light, and their commitment to work hard or more effectively at their
relationships (Carver et al., 2010).
Much research has been done studying the
importance of optimism in the context of medical
settings. Findings show that more optimistic people tend to report less distress in adverse medical
situations, including coronary artery bypass surgery, cancer, bone marrow transplantation, and
failed attempts with in-vitro fertilization (for
review, see Carver et al., 2010). Findings also
indicate that optimism appears to help patients
and their caregivers be more resistant to depressive symptoms as well as adjust to difficult life
circumstances in a more constructive way.

Optimism, Pessimism, and Coping
Differences in coping have been suggested as one
of the essential factors explaining differences
between optimists and pessimists. Optimism
was found to be related to approach- and problem-oriented coping, while pessimism was found
to be related to avoidance- and emotion-focused

Optimism-Pessimism, Dispositional

coping (Solberg Nes & Segerstrom, 2006).
Solberg Nes and Segerstrom (2006) reviewed
findings showing that, in addition to problemfocused coping strategies (e.g., planning, seeking
instrumental support), optimists, in certain
situations, use even emotion-focused coping
(e.g., cognitive restructuring, seeking emotional
support), which might be more powerful.
That is, when confronting controllable stressors
(e.g., health stressor), optimists tend to use more
problem-oriented coping and show attempts to
change their efforts, while when confronting
uncontrollable stressors (e.g., traumatic event),
they tend to use more emotion-oriented coping
and show attempts to accommodate to stressful
circumstances (Solberg Nes & Segerstrom,
2006). Furthermore, coping appears to be
a significant and important mediator/moderator
in the relationship between optimism and mental
health. Findings have indicated that the positive
effect of optimism on mental health occurs
through an indirect effect of differences in coping
(for a review, see Carver et al., 2010). Moreover,
optimists are likely to report that they benefit
from adverse experiences by, for example,
becoming closer to their spouse (Litt, Tennen,
Affleck, & Klock, 1992) – a domain that has
been identified in posttraumatic growth. Furthermore, optimists appear to use coping strategies
that not only help them react in a more helpful
way in adverse situations but also prevent
a stressor from arising – called proactive coping
(Aspinwall & Taylor, 1997). Carver et al. (2010)
reviewed studies showing that more optimistic
people sought heart-attack-related knowledge
pertaining to areas of potential risk, increased
physical exercising to prevent risks for coronary
diseases, avoided certain sexual practices to
decrease HIV risk, etc. Optimists, as Carver et al.
(2010) expressed, not only expect that good things
are going to happen, but they also take active steps
to make sure good things do happen.

4505

optimism; that is, do optimists/pessimists remain
optimists/pessimists over the life course?
Optimism is trait-like and therefore tends to be
rather stable over time (Carver & Scheier, 2002).
However, some periods (e.g., times of life transitions) appear to be more critical with possible
dips in optimism. Segerstrom (2007) reported
a stability coefficient of 0.35 over a 10-year
period. The coefficient is much lower than what
has been reported by other researchers (e.g., 0.71
by Matthews et al., 2004); however, it is likely
due to the studied sample – young people that
went across a period of time of considerable
changes in their life circumstances (from studies
at law school to engagement in their law
practices).
The second question involves the cultivation
of optimism; that is, is it possible for a pessimistic
person to move on the optimism-pessimism
continuum and become more optimistic? Some
studies have shown promising results that this
kind of change is possible (Segerstrom, 2007),
using different techniques from cognitive
behavioral therapies. However, most often the
techniques in cognitive therapy aim to reduce
negative thoughts and therefore change negative
cognitive schemas about a person’s sense of
self and the world. Recently researchers have
started focusing on interventions that directly
target positive cognitions. Findings by, for
example, Fosnaugh, Geers, and Wellman (2009)
and Meevissen, Peters, and Alberts (2011)
show that such interventions were successful
increasing people’s positive expectations about
their future. Carver et al. (2010), however, question how large a change can reasonably be
expected from a person and how permanent the
change will be. Thus, more research is needed to
evaluate the benefits and durability of induced
optimism.

Cross-References
Changes in Optimism
Two questions regarding changes in optimism
are of interest. The first involves the stability of

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▶ Positive and Negative Affect Schedule
(PANAS)
▶ Subjective Well-Being

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4506

Optimization Theory

References

of the life orientation test. Journal of Personality and
Social Psychology, 67, 1063–1078.
Schimmack, U., Diener, E., & Oishi, S. (2002). Lifesatisfaction is a momentary judgment and a stable
personality characteristic: The use of chronically
accessible and stable sources. Journal of Personality,
70, 345–385.
Segerstrom, S. C. (2007). Optimism and resources: Effects
on each other and on health over 10 years. Journal of
Research in Personality, 41, 772–786.
Solberg Nes, L., & Segerstrom, S. C. (2006). Dispositional
optimism and coping: A meta-analytic review.
Personality and Social Psychology Review,
10, 235–251.

Aspinwall, L. G., & Taylor, S. E. (1997). A stitch in time:
Self-regulation and proactive coping. Psychological
Bulletin, 121, 417–436.
Carver, C. S., & Scheier, M. F. (2002). Optimism,
pessimism, and self-regulation. In E. C. Chang (Ed.),
Optimism & pessimism. Implications for theory,
research, and practice (pp. 31–52). Washington, DC:
American Psychological Association.
Carver, C. S., Scheier, M. F., & Segerstrom, S. C. (2010).
Optimism. Clinical Psychology Review, 30, 879–889.
Chang, E. C., & Sanna, L. J. (2001). Optimism,
pessimism, and positive and negative affectivity in
middle aged adults: A test of a cognitive-affective
model of psychological adjustment. Psychology and
Aging, 16, 524–531.
Daukantaite, D., & Bergman, L. R. (2005). Childhood
roots of women’s subjective well-being: The role of
optimism. European Psychologist, 10, 287–297.
Fosnaugh, J., Geers, A. L., & Wellman, J. A. (2009).
Giving of a rosy glow: The manipulation of an
optimistic orientation. The Journal of Social
Psychology, 149, 249–263.
Litt, M. D., Tennen, H., Affleck, G., & Klock, S. (1992).
Coping and cognitive factors in adaptation to in vitro
fertilization failure. Journal of Behavioral Medicine,
15, 171–187.
Lucas, R. E., Diener, E., & Suh, E. (1996). Discriminant
validity of well-being measures. Journal of
Personality and Social Psychology, 71, 616–628.
Marshall, G. N., Wortman, C. B., Kusulas, J. W., Hervig,
L. K., & Vickers, R. R., Jr. (1992). Distinguishing
optimism from pessimism: Relations to fundamental
dimensions of mood and personality. Journal of
Personality and Social Psychology, 62, 1067–1074.
Matthews, K. A., R€aikkönen, K., Sutton-Tyrrell, K., &
Kuller, L. H. (2004). Optimistic attitudes protect
against progression of carotid atherosclerosis in
healthy
middle-aged women.
Psychosomatic
Medicine, 66, 640–644.
Meevissen, Y. M. C., Peters, M. L., & Alberts, H. J. E. M.
(2011). Become more optimistic by imagining
a best possible self: Effects of a two week intervention.
Journal of Behavior Therapy and Experimental
Psychiatry, 42, 371–378.
Robinson-Whelen, S., Kim, C., MacCallum, R., &
Kiecolt-Glaser, J. K. (1997). Distinguishing
optimism from pessimism in older adults: Is it more
important to be optimistic or to not be pessimistic?
Journal of Personality and Social Psychology, 73,
1345–1353.
Scheier, M. F., & Carver, C. S. (1985). Optimism, coping,
and
health:
Assessment
and
implications
of generalized outcome expectancies. Health
Psychology, 4, 219–247.
Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994).
Distinguishing optimism from neuroticism (and trait
anxiety, self-mastery, and self-esteem): A reevaluation

Optimization Theory
▶ Rational Choice Theory

Oral Health Impact Profile
Fotios Anagnostopoulos
Department of Psychology, Panteion University
of Social & Political Sciences, Athens, Greece

Definition
This is a 49-item measure of oral health-related
quality of life, which measures people’s perception of the impact of oral disorders on their
well-being, that is, the dysfunction, discomfort,
disability, and handicap caused by oral conditions.

Description
The Oral Health Impact Profile (OHIP) was developed by Slade and Spencer (1994) as a self-rating
patient-centered instrument designed to assess the
priorities of care by documenting social impact
among individuals and groups, understand oral
health behaviors, evaluate dental treatment, and
provide information for planning for oral health.
Seven dimensions are captured by the
OHIP-49: Functional Limitation (9 items,

Oral Health Impact Profile

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Functional limitation

Disease

Disability
(physical,
psychological,
social)

Impairment

Handicap

Discomfort & Pain

Oral Health Impact Profile, Fig. 1 Locker’s conceptual model of oral health

e.g., “Have you had trouble pronouncing any
words because of problems with your teeth,
mouth, dentures, or jaw?” “Have you had food
catching in your teeth because of problems with
your teeth, mouth, or dentures?”), Physical Pain
(9 items, e.g., “Have you had painful aching in
your mouth because of problems with your teeth,
mouth, or dentures?” “Have you had sensitive
teeth with hot or cold food or drinks because of
problems with your teeth, mouth, or dentures?”),
Psychological Discomfort (5 items, e.g., “Have
you felt tense because of problems with your
teeth, mouth, or dentures?” “Have you been
worried by dental problems?”), Physical Disability (9 items, e.g., “Have you had to interrupt
meals because of problems with your teeth,
mouth, or dentures?” “Have you had to avoid
eating some foods because of problems with
your teeth, mouth, or dentures?”), Psychological
Disability (6 items, e.g., “Have you found it difficult to relax because of problems with your
teeth, mouth, or dentures?” “Have you been
embarrassed because of problems with your
teeth, mouth, or dentures?”), Social Disability
(5 items, e.g., “Have you had difficulty doing
your usual jobs because of problems with your
teeth, mouth, or dentures?” “Have you avoided
going out because of problems with your teeth,
mouth, or dentures?”), and Handicap (6 items,
e.g., “Have you felt that life in general was less
satisfying because of problems with your teeth,
mouth, or dentures?”). Instructions for the OHIP
ask respondents how frequently they had experienced each problem in the preceding 12 months.
Responses are made on a 5-point Likert-type

scale ranging from “never” (0) to “very
often” (4). The OHIP-49 has been translated
from English into many languages including
German (John, Patrick, & Slade, 2002),
Japanese (Yamazaki, Inukai, Baba, & John,
2007), Swedish (Larsson, List, Lundström,
Marcusson, & Ohrbach, 2004), Dutch (van der
Meulen, John, Naeije, & Lobbezoo, 2008), and
Arabic (Al-Jundi, Szentpétery, & John, 2007).
One of the strengths of OHIP is that the seven
dimensions described above are based on
Locker’s conceptual model of oral health
(Adulyanon, & Sheiham, 1997; Allen, 2003;
Baker, 2007; Locker, 1988; Nuttall, Steele,
Pine, White, & Pitts, 2001). This model, in turn,
uses the World Health Organization’s International Classification of Impairments, Disabilities,
and Handicaps (ICIDH) definitions of disease
states (World Health Organisation, 1980).
Locker’s model proposes that impacts of disease
are sorted into a hierarchy that there are five
consequences of oral disease (impairment, functional limitation, pain and discomfort, disability,
and handicap) and that these are sequentially
related (Fig. 1).
Disease states (e.g., a detectable dental
disease) may lead to impairments (e.g., anatomical loss, structural abnormality, or disturbance,
such as edentulousness, or missing teeth), which
can result in functional limitation (e.g., restricted
jaw mobility and difficulty chewing, trouble pronouncing words, worsened sense of taste) or
discomfort and pain (e.g., painful gums), which
will in turn result in physical, psychological, or
social disability (limitation in or lack of ability to

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perform daily activities such as unsatisfactory
diet, concentration problems, avoiding smiling,
embarrassment, problems in social contacts, and
social intimacy), which may lead to the subsequent handicap for the individuals (social disadvantage experienced by impaired and disabled
people because they do not conform to the expectations of society to which they belong, such as
loss of employment opportunities due to an
inability to communicate effectively, being
unable to enjoy other people’s company due to
the embarrassment of complete denture wearing,
life dissatisfaction), ultimately impacting on their
quality of life. Impairment, discomfort, and functional limitation may also lead directly to
handicap.
To provide an empirical test of the ▶ construct
validity of the OHIP-49 as a measure of Locker’s
conceptual model of oral health, Baker, Gibson,
and Locker (2008) carried out an analysis of data
drawn from a nonclinical sample of Canadian
adult population (N ¼ 541) using structural
equation modeling. The findings indicated that
the OHIP-49 does not have adequate
within-construct validity, since scale items did
not always measure the construct/dimension
they were supposed to measure, some items
within a construct were redundant, many measured more than one constructs, and the instrument did not represent seven separate constructs
of oral health as originally devised (e.g., discomfort and psychological disability showed poor
▶ discriminant validity and were overlapping).
Although a revised six-factor 22-item version
had a better fit to the data, it did not have adequate
between-construct validity since many of the
relationships between the constructs/dimensions,
as hypothesized within Locker’s conceptual
model, were not supported (e.g., functional limitations and pain were not predictive of physical or
social disability, physical disability was not
linked to handicap, while functional limitations
were linked indirectly to handicap via pain and
psychological impacts). Thus, the authors concluded that further conceptual development of
the instrument, and Locker’s model, is needed
in order to understand the complexity of oral
health from a patient-centered perspective.

Oral Health Impact Profile

Reliability
▶ Internal consistency was assessed by computing Cronbach’s alpha coefficients derived from
the original Australian data set of persons. These
alpha coefficients were satisfactory for six subscales (a ¼ 0.70–0.83) but only 0.37 for handicap. ▶ Test-retest reliability was evaluated by
computing intraclass correlation coefficients,
which ranged from 0.42 to 0.77 for six of the
subscales but only 0.08 for social disability
(Slade, & Spencer, 1994).
Validity

The OHIP-49 has been shown to detect an
association between subscale scores and perceived need for a dental visit, which provided
evidence of its ▶ construct validity (Slade, &
Spencer, 1994).
The OHIP-14
To compensate for some of the limitations
encountered with the original OHIP-49 instrument, shorten it, and eliminate items that apply
only to those who wear dentures, as well as items
which have a nonresponse rate (left blank or
marked “don’t know”) of 5 % or more, the
short-form OHIP-14 was developed by Gary
Slade (1997) using a controlled stepwise regression procedure. In the subset of 14 questions from
the full 49 questions, two items from each of the
seven dimensions were retained. Responses are
made on a 5-point scale, coded 0 (never),
1 (hardly ever), 2 (occasionally), 3 (fairly
often), and 4 (very often). The reference period
is the previous year. Weights are computed that
represent the proportion of people who judge the
impact within each dimension as more unpleasant
than the other impact in that dimension. For
example, within the social disability dimension,
the weight for the item “Have you had difficulty
doing your usual jobs because of problems with
your teeth, mouth, or dentures?” is equal to 0.38,
while the weight for the item “Have you been
a bit irritable with other people because of problems with your teeth, mouth, or dentures?” is
equal to 0.62. Weights are then multiplied by
coded responses to each question, and the
products are added to yield seven subscale scores

Oral Health Impact Profile

that are standardized and summed. Three summary measures can also be computed: prevalence, severity, and extent (Slade et al., 2005).
Prevalence scores are derived by using
a threshold of “fairly often” or “very often” to
one or more items to dichotomize responses,
thereby indicating the percentage of people who
report chronic oral health impacts. Severity
scores are derived by summing ordinal responses
for all 14 items and additionally taking into
account impacts experienced “occasionally” or
“hardly ever.” Extent is summarized for each
survey participant by the number of items
reported “fairly often” or “very often.” The
OHIP-14 accounts for 94 % of the variation in
total OHIP-49 scores, while it has an internal
reliability coefficient alpha equal to 0.88
(Slade, 1997). The OHIP-14 has been translated
and adapted into different languages including
Brazilian (de Oliveira, & Nadanovsky, 2005),
Greek (Papagiannopoulou, Oulis, Papaioannou,
Antonogeorgos, & Yfantopoulos, 2012), Hebrew
(Kushnir, Zusman, & Robinson, 2004), and
Persian (Ravaghi, Farrahi-Avval, Locker, &
Underwood, 2010).

Discussion
OHIP remains one of the most robust tools for
assessing the impact of oral health on an individual’s life. Epidemiological studies have
shown that researchers can expect higher OHIP
scores in patients with poorer clinical oral status,
that is, patients with more missing teeth, more
retained root fragments, more untreated decay,
deeper periodontal pockets, and more periodontal recession. OHIP scores are also higher in
socially and economically disadvantaged groups
and among those who are irregular dental
attenders (Slade et al., 1996). There is some
evidence that the events captured by the OHIP
may be of broader significance than oral health
since OHIP scores may be associated with ▶ life
satisfaction, general ▶ well-being, and ▶ quality of life. However, other investigators
argue that this may not be the case (Locker, &
Allen, 2007).

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Cross-References
▶ Construct Validity
▶ Discriminant Validity
▶ Internal Consistency Reliability
▶ Intraclass Correlation Coefficient (ICC)
▶ Life Satisfaction
▶ Quality of Life
▶ Test-Retest Reliability
▶ Well-Being

References
Adulyanon, S., & Sheiham, A. (1997). Oral impacts on
daily performances. In G. D. Slade (Ed.), Measuring
oral health and quality of life (pp. 151–160). Chapel
Hill, NC: University of North Carolina, Department of
Dental Ecology.
Al-Jundi, M. A., Szentpétery, A., & John, M. T. (2007).
An Arabic version of the oral health impact profile:
Translation and psychometric properties. International Dental Journal, 57(2), 84–92.
Allen, P. F. (2003). Assessment of oral health related quality of life. Health and Quality of Life Outcomes, 1, 40.
Baker, S. R. (2007). Testing a conceptual model of oral
health: A structural equation modeling approach.
Journal of Dental Research, 86(8), 708–712.
Baker, S. R., Gibson, B., & Locker, D. (2008). Is the oral
health impact profile measuring up? Investigating the
scale’s construct validity using structural equation
modelling. Community Dentistry and Oral Epidemiology, 36, 532–541.
de Oliveira, B. H., & Nadanovsky, P. (2005). Psychometric properties of the Brazilian version of the oral health
impact profile- short form. Community Dentistry and
Oral Epidemiology, 33(4), 307–314.
John, M. T., Patrick, D. L., & Slade, G. D. (2002). The
German version of the oral health impact profiletranslation and psychometric properties. European
Journal of Oral Sciences, 110(6), 425–433.
Kushnir, D., Zusman, S. P., & Robinson, P. G. (2004).
Validation of a Hebrew version of the oral health
impact profile 14. Journal of Public Health Dentistry,
64(2), 71–75.
Larsson, P., List, T., Lundström, I., Marcusson, A., &
Ohrbach, R. (2004). Reliability and validity of a Swedish
version of the oral health impact profile (OHIP-S). Acta
Odontologica Scandinavica, 62(3), 147–152.
Locker, D. (1988). Measuring oral health: A conceptual
framework. Community Dental Health, 5(1), 3–18.
Locker, D., & Allen, F. (2007). What do measures of “oral
health-related quality of life” measure? Community
Dentistry and Oral Epidemiology, 35, 401–411.
Nuttall, N. M., Steele, J. G., Pine, C. M., White, D., & Pitts,
N. B. (2001). The impact of oral health on people in the
UK in 1998. British Dental Journal, 190(3), 121–126.

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Papagiannopoulou, V., Oulis, C. J., Papaioannou, W.,
Antonogeorgos, G., & Yfantopoulos, J. (2012). Validation of a Greek version of the oral health impact
profile (OHIP-14) for use among adults. Health and
Quality of Life Outcomes, 10, 7.
Ravaghi, V., Farrahi-Avval, N., Locker, D., & Underwood,
M. (2010). Validation of the Persian short version of the
oral health impact profile (OHIP-14). Oral Health &
Preventive Dentistry, 8(3), 229–235.
Slade, G. D. (1997). Derivation and validation of a shortform oral health impact profile. Community Dentistry
and Oral Epidemiology, 25, 284–290.
Slade, G. D., Nuttall, N., Sanders, A. E., Steele, J. G.,
Allen, P. F., & Lahti, S. (2005). Impacts of oral disorders in the United Kingdom and Australia. British
Dental Journal, 198(8), 489–493.
Slade, G. D., & Spencer, A. J. (1994). Development and
evaluation of the Oral Health Impact Profile. Community Dental Health, 11(1), 3–11.
Slade, G. D., Spencer, A. J., Locker, D., Hunt, R. J.,
Strauss, R. P., & Beck, J. D. (1996). Variations in the
social impact of oral conditions among older adults in
South Australia, Ontario, and North Carolina. Journal
of Dental Research, 75(7), 1439–1450.
van der Meulen, M. J., John, M. T., Naeije, M., &
Lobbezoo, F. (2008). The Dutch version of the oral
health impact profile (OHIP-NL): Translation, reliability and construct validity. BMC Oral Health, 8, 11.
World Health Organisation. (1980). International classification of impairments, disabilities and handicaps.
Geneva: WHO.
Yamazaki, M., Inukai, M., Baba, K., & John, M. T. (2007).
Japanese version of the oral health impact profile
(OHIP-J). Journal of Oral Rehabilitation, 34(3), 159–168.

Order, Sibling
▶ Birth Order

Ordered Logit Model
Leonardo Grilli and Carla Rampichini
Department of Statistics ‘G. Paernti’, University
of Florence, Florence, Italy

Synonyms
Cumulative logistic regression; Cumulative logit
model; Ordinal logistic regression; Proportional
odds model

Order, Sibling

Definition
The ordered logit model is a regression model for
an ordinal response variable. The model is based
on the cumulative probabilities of the response
variable: in particular, the logit of each cumulative probability is assumed to be a linear function
of the covariates with ▶ Regression Coefficients
constant across Response Categories.

Description
Questions relating to satisfaction with life
assessment and expectations are usually ordinal
in nature. For example, the answer to the
question on how satisfied a person is with her
quality of life can range from 1 to 10, with one
being very dissatisfied and ten being very satisfied
(e.g., Anderson, Mikuliç, Vermeylen, LylyYrjanainen, & Zigante, 2009; Schaafsma &
Osoba, 1994). It is tempting to analyze ordinal
outcomes with the linear regression model, assuming equal distances between categories. However,
this approach has several drawbacks which are
well known in literature (see, e.g., Lu, 1999;
McKelvey & Zavoina, 1975; Winship & Mare,
1984). When the response variable of interest is
ordinal, it is advisable to use a specific model such
as the ordered logit model.
Let Yi be an ordinal response variable with
C categories for the i-th subject, alongside
with a vector of covariates xi. A regression
model establishes a relationship between the
covariates and the set of probabilities of the
categories
pci ¼ PrðYi ¼ yc jxi Þ; c ¼ 1; . . . ; C.
Usually, regression models for ordinal responses
are not expressed in terms of probabilities of
the categories, but they refer to convenient oneto-one transformations, such as the cumulative
probabilities gci ¼ PrðYi  yc jxi Þ; c ¼ 1; . . . ; C.
Note that the last cumulative probability is
necessarily equal to 1, so the model specifies
only C1 cumulative probabilities.
An ordered logit model for an ordinal response
Yi with C categories is defined by a set of C1
equations where the cumulative probabilities
gci ¼ PrðYi  yc jxi Þ are related to a linear

Ordered Logit Model

predictor
b0 xi ¼ b0 þ b1 x1i þ b2 x2i þ . . .
through the logit function:
logitðgci Þ ¼ logðgci =ð1  gci ÞÞ
¼ ac  b0 xi ; c ¼ 1; 2; . . . ; C  1 (1)
The parameters ac, called thresholds or
cutpoints,
are
in
increasing
order
ða1 < a2 < . . . < aC1 Þ. It is not possible to simultaneously estimate the overall intercept b0 and all
the C1 thresholds: in fact, adding an arbitrary
constant to the overall intercept b0 can be
counteracted by adding the same constant to
each threshold ac. This identification problem is
usually solved by either omitting the overall
constant from the linear predictor (i.e., b0 ¼ 0)
or fixing the first threshold to zero (i.e., a1 ¼ 0).
The vector of the slopes b is not indexed by
the category index c; thus, the effects of
the covariates are constant across response
categories. This feature is called the parallel
regression assumption; indeed, plotting logit
(gci) against a covariate yields C1 parallel
lines (or parallel curves in case of a nonlinear
specification, e.g., polynomial regression). In
model (1), the minus before b implies that
increasing a covariate with a positive slope is
associated with a shift towards the right end of
the response scale, namely, a rise of the probabilities of the higher categories. Some authors write
the model with a plus before b; in that case, the
interpretation of the effects of the covariates is
reversed.
From Equ. (1), the cumulative probability for
category c is
gci ¼ expðac  b0 xi Þ=ð1 þ expðac  b0 xi ÞÞ
¼ 1=ð1 þ expðac þ b0 xi ÞÞ
(2)
The ordered logit model is also known as the
proportional odds model because the parallel
regression assumption implies the proportionality of the odds of not exceeding the c-th category
oddsci ¼ gci =ð1  gci Þ: in fact, the ratio of these
odds for two units, say i and j, is
 


oddsci =oddscj ¼ exp b0 xj  xi , which does
not depend on c and thus it is constant across
response categories.

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The ordered logit model is a member of the
wider class of cumulative ordinal models, where
the logit function is replaced by a general link
function. The most common link functions are
logit, probit, and complementary log-log. These
models are known in psychometrics as graded
response models (Samejima, 1969) or difference
models (Thissen & Steinberg, 1986). The last
name indicates that the probabilities of the
categories are obtained by difference
pci ¼ gci  gðc1Þ ;i .
Early papers on regression models for ordinal
data include McKelvey and Zavoina (1975),
McCullagh (1980), and Winship and Mare
(1984). The paper of Fullerton (2009) reviews
ordered logistic regression models and their use
in sociology. The textbook of Agresti (2010)
gives a thorough treatment of ordinal data,
while O’Connell (2006) provides applied
researchers in the social sciences with accessible
and comprehensive coverage of analyses for ordinal outcomes. Other valuable books fully devoted
to ordinal outcomes are Johnson and Albert
(1999) in a Bayesian perspective and Greene
and Hensher (2010) in the setting of choice
theory. Books on statistical modeling often have
a chapter on ordinal regression models, for example, Long (1997), Skrondal and Rabe-Hesketh
(2004), and Hilbe (2009).
Representation as an Underlying Linear
Model with Thresholds
An ordinal response Yi with C categories can be
represented as an underlying continuous response
Yi* with a set of C1 thresholds ac* such that
Yi ¼ yc if and only if ac 1 < Yi   ac  . It follows
that a cumulative model for an ordinal response,
such as the ordered logit model (1), is equivalent
to a system composed of a set of thresholds ac*
and a linear regression model for an underlying
continuous response:
Yi ¼ ðb Þ0 xi þ ei

(3)

where ei* is an error with mean zero and
standard deviation se*. The relationship
PrðYi  yc Þ ¼ PrðYi   ac  Þ implies that the

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Ordered Logit Model

linear model (3) is equivalent to the cumulative
model lðgci Þ ¼ ac  b0 xi , where the link function
l(	) is the inverse of the distribution function of
the error ei*.
The relationship between a parameter of the
cumulative model y and the corresponding
parameter of the underlying model y* is
y ¼ y sl =se , where sl is the standard deviation
of the distribution associated to the
ffiffiffi function
plink
(e.g., sl ¼ 1 for probit and sl ¼ p 3 
 1:81 for
logit). Therefore, specifying the link function of
the cumulative model amounts to specifying
the distribution of the error of the underlying
model and thus fixing its standard deviation to
a conventional value: the probit corresponds to
a standard normal error so the standard deviation
is fixed to 1, whereas the logit link corresponds
to a standard logistic distribution
pffiffiffi so the
standard deviation is fixed to p 3 
 1:81).
Indeed, the measurement unit of the
underlying
model
is
undefined
since
PrðYi   ac  Þ ¼ PrðkYi   kac  Þ for any constant k; thus, the standard deviation se* is not
identifiable. This indeterminacy is solved in the
cumulative model (1) since its parameters are
measured on a conventional scale defined by the
link (the standard deviation of the error does not
appear as a parameter). The change of scale is the
reason why the estimated regression coefficients
from an ordered logit model are about 1.81 times
the values from an ordered probit model.
The representation through an underlying
linear model also makes clear that the estimated
slopes from a cumulative model are approximately invariant to merging of the categories.

covariates (McCullagh, 1980) or, alternatively,
to use a scaled link such as the scaled probit
link of Skrondal and Rabe-Hesketh (2004).
A further approach is to introduce latent classes
(Breen & Luijkx, 2010). Models violating the
parallel regression assumption should be used
with care since they raise identification and interpretation issues (Agresti, 2010).

Relaxing the Parallel Regression Assumption
The parallel regression assumption of the cumulative models may be too restrictive (for a test,
see Brant, 1990). Such an assumption can be
relaxed by allowing the thresholds to depend on
covariates or, alternatively, by allowing
covariates to have category-specific slopes.
These models are called partial proportional
odds after Peterson and Harrell (1990). Another
way to relax the parallel regression assumption is
to let the variance of the disturbance ei* in the
underlying linear model (3) to depend on

References

Multilevel Extension
Multilevel (random effects) ordered logit models
are suitable for the analysis of correlated ordinal
responses; see the reviews of Agresti and
Natarajan (2001), Hedeker (2008), and Grilli
and Rampichini (2011). Multilevel ordered logit
or probit models may be useful in several kinds of
applications in quality of life, for example,
(1) analysis of a single response from individuals
clustered into households, schools (e.g., Fielding,
Yang & Goldstein, 2003), or geographical
regions (e.g., Rampichini & Schifini, 1998);
(2) joint analysis of a set of items of a survey
questionnaire on individuals (e.g., Grilli &
Rampichini, 2003); and (3) analysis of repeated
responses to a given question in a longitudinal
survey (e.g., Ribaudo et al., 1999).

Cross-References
▶ Hierarchical Linear Modeling
▶ Linear Regression Model
▶ Multilevel Analysis
▶ Odds Ratio

Agresti, A. (2010). Analysis of ordinal categorical data
(2nd ed.). New York: Wiley.
Agresti, A., & Natarajan, R. (2001). Modeling clustered
ordered categorical data: A survey. International
Statistical Review, 69, 345–371.
Anderson, R., Mikuliç, B., Vermeylen, G., Lyly-Yrjanainen,
M., & Zigante, V. (2009). Second European quality-oflife survey: Overview. Luxembourg: Office for Official
Publications of the European Communities.
Brant, R. (1990). Assessing proportionality in the proportional odds model for ordinal logistic regression.
Biometrics, 46, 1171–1178.

Ordinal Alpha
Breen, R., & Luijkx, R. (2010). Mixture models for ordinal data. Sociological Methods & Research, 39, 3–24.
Fielding, A., Yang, M., & Goldstein, H. (2003). Multilevel
ordinal models for examination grades. Statistical
Modelling, 3, 127–153.
Fullerton, A. S. (2009). A conceptual framework for
ordered logistic regression models. Sociological
Methods & Research, 38, 306–347.
Greene, W. H., & Hensher, D. A. (2010). Modeling
ordered choices: A primer. Cambridge: Cambridge
University Press.
Grilli, L., & Rampichini, C. (2003). Alternative specifications of multivariate multilevel probit ordinal response
models. Journal of Educational and Behavioral Statistics, 28, 31–44.
Grilli, L., & Rampichini, C. (2011). Multilevel models for
ordinal data. In R. Kenett & S. Salini (Eds.), Modern
analysis of customer satisfaction surveys. New York:
Wiley.
Hedeker, D. (2008). Multilevel models for ordinal and
nominal variables. In J. De Leeuw & E. Meijer
(Eds.),
Handbook
of
multilevel
analysis
(pp. 237–274). New York: Springer.
Hilbe, M. H. (2009). Logistic regression models. London:
Chapman & Hall/CRC.
Johnson, V. E., & Albert, J. H. (1999). Ordinal data
modeling. New York: Springer.
Long, S. (1997). Regression models for categorical and
limited dependent variables. Thousand Oaks, CA:
Sage.
Lu, M. (1999). Determinants of residential satisfaction:
Ordered logit vs. regression models. Growth and
Change, 30, 264–87. questo non viene citato.
McCullagh, P. (1980). Regression models for ordinal data.
Journal of the Royal Statistical Society Series B, 42,
109–142.
McKelvey, R. D., & Zavoina, W. (1975). A statistical
model for the analysis of ordinal level dependent
variables. Journal of Mathematical Sociology, 4,
103–120.
O’Connell, A. A. (2006). Logistic regression models for
ordinal response variables. Thousand Oaks, CA: Sage.
Peterson, B., & Harrell, F. E. (1990). Partial proportional
odds models for ordinal response variables. Applied
Statistics, 39, 205–217.
Rampichini, C., & Schifini, S. (1998). A hierarchical ordinal probit model for the analysis of life satisfaction in
Italy. Social Indicators Research, 44, 5–39.
Samejima, F. (1969). Estimation of latent trait ability
using a response pattern of graded scores. Psychometric Monograph 17, Psychometric Society, Bowling
Green, OH.

Order-Preserving Mapping
▶ Multiple Scaling

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Ordinal Alpha
Anne Gadermann1, Martin Guhn2 and
Bruno D. Zumbo3
1
Health Care Policy, Harvard Medical School,
Boston, MA, USA
2
Human Early Learning Partnership, School of
Population and Public Health, University of
British Columbia, Vancouver, BC, Canada
3
Department of ECPS & Department of
Statistics, Institute of Applied Mathematics,
University of British Columbia, Vancouver,
BC, Canada

Synonyms
Internal consistency; Reliability coefficient

Definition
Ordinal coefficient alpha estimates the internal
consistency for scales involving ordinal data,
using the polychoric correlation matrix for its
estimation.

O
Description
▶ Reliability and validity are fundamental properties of measurement scales and tests.
▶ Cronbach’s alpha (Cronbach, 1951), a method
to estimate ▶ internal consistency, is the most
widely and frequently used reliability index
(Zumbo & Rupp, 2004). Reliability coefficients,
such as Cronbach’s alpha, are typically calculated using a Pearson covariance matrix (e.g., as
a default in statistical software programs, such as
SPSS). One assumption when using the Pearson
correlation (or more generally, covariance)
matrix is that the data are continuous; if this is
not the case, the Pearson correlation matrix may
be significantly distorted (e.g., Flora & Curran,
2004).
In quality of life measurement, it is very common to use ordinal item response data, e.g., based

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4514

on the Likert-type item response format with 5 or
7 response options. For example, for the ▶ Satisfaction with Life Scale (Diener, Emmons,
Larsen, & Griffin, 1985), respondents are asked
to indicate their level of agreement to five items,
such as “I am satisfied with my life,” by choosing
one out of seven ordered response options ranging between “strongly agree” and “strongly disagree.” Data from such items are ordinal and not
continuous but are often treated as if they were
continuous. This is problematic because the Pearson correlation coefficient may significantly
underestimate the strength of association
between two continuous variables when the two
variables manifest themselves in a skewed distribution of observed responses. In contrast, the
polychoric correlation is more accurate in estimating the strength of association of the underlying variables (Carroll, 1961). Given these
findings, the polychoric correlation matrix is considered more appropriate for ordinal data.
In line with this, Cronbach’s alpha may significantly underestimate the reliability of a scale
when its assumptions are violated (e.g., Gelin,
Beasley, & Zumbo, 2003; Maydeu-Olivares,
Coffman, & Hartmann, 2007; Osburn, 2000).
This is important insofar, as underestimating the
reliability of a scale my lead to inferences that are
not warranted; for example, based on a low
Cronbach’s alpha, researchers may consider
a scale too unreliable, even though the scale’s
actual reliability may be sufficient for the scale’s
given purpose or use.
In order to address this issue, Zumbo,
Gadermann, and Zeisser (2007) introduced ordinal coefficient alpha, which is conceptually
equivalent to Cronbach’s alpha, but is estimated
using the polychoric rather than the Pearson correlation matrix. In a simulation study, Zumbo,
Gadermann, and Zeisser (2007) showed that ordinal alpha estimates reliability more accurately
than Cronbach’s alpha for binary and ordinal
response scales. (KR-20, a special case of coefficient alpha, is computed from binary data.) The
study demonstrated that ordinal alpha is
a consistently suitable estimate of the (simulated)
theoretical reliability, irrespective of the magnitude of the theoretical reliability, the number of

Ordinal Alpha

response options, and the skewness of the scale
point distributions. Cronbach’s alpha, however,
was shown to be a negatively biased estimate of
reliability. This underestimation increased with
increasing skewness, fewer response options, and
lower theoretical reliability. With a real data
example using data from a subscale of the
▶ Early Development Instrument (Janus &
Offord, 2007) with binary items that were highly
skewed, Gadermann, Guhn, and Zumbo (2012)
demonstrated that Cronbach’s alpha and ordinal
alpha can be as different as .46 versus .85,
respectively.
The approach of using the polychoric correlation matrix to account for the ordinal nature of the
data can be generalized to any reliability estimate
that is calculated from a correlation matrix, such
as coefficient omega or beta. These ordinal reliability coefficients can be easily calculated using
software programs that provide the polychoric
correlation matrix. For example, a tutorial by
Gadermann, Guhn, and Zumbo (2012) illustrates
how to calculate ordinal alpha and other ordinal
reliability coefficients, such as omega or beta, in
the freely available software R, and a paper by
Elosua Oliden and Zumbo (2008) provides syntax for calculating ordinal alpha in Mplus or
PRELIS/LISREL.

Cross-References
▶ Cronbach’s Alpha
▶ Early Development Instrument
▶ Internal Consistency
▶ Reliability
▶ Satisfaction with Life Scale (SWLS), an
Overview

References
Carroll, J. B. (1961). The nature of data, and how to
choose a correlation coefficient. Psychometrika, 26,
347–372.
Cronbach, L. J. (1951). Coefficient alpha and the internal
structure of tests. Psychometrika, 16, 297–334.

Ordinary Least-Squares (OLS) Model
Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S.
(1985). The satisfaction with life scale. Journal of
Personality Assessment, 49, 71–75.
Elosua Oliden, P., & Zumbo, B. D. (2008). Coeficientes de
fiabilidad para escalas de respuesta categorica
ordenada. Psicothema, 20, 896–901.
Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data.
Psychological Methods, 9, 466–491.
Gadermann, A. M., Guhn, M., & Zumbo, B. D. (2012).
Estimating ordinal reliability for Likert-type and ordinal item response data: A conceptual, empirical, and
practical guide. Practical Assessment, Research &
Evaluation, 17, 1–13.
Gelin, M. N., Beasley, T. M., & Zumbo, B. D. (2003,
April). What is the impact on scale reliability and
exploratory factor analysis of a Pearson correlation
matrix when some respondents are not able to follow
the rating scale? Paper presented at the annual meeting
of the American Educational Research Association
(AERA) in Chicago, Illinois.
Janus, M., & Offord, D. (2007). Development and psychometric properties of the early development instrument (EDI): A measure of children’s school readiness.
Canadian Journal of Behavioral Science, 39, 1–22.
Maydeu-Olivares, A., Coffman, D. L., & Hartmann,
W. M. (2007). Asymptotically distribution free
(ADF) interval estimation of coefficient alpha.
Psychological Methods, 12, 157–176.
Osburn, H. G. (2000). Coefficient alpha and related internal consistency reliability coefficients. Psychological
Methods, 5, 343–355.
Zumbo, B. D., Gadermann, A. M., & Zeisser, C. (2007).
Ordinal versions of coefficients alpha and theta for
Likert rating scales. Journal of Modern Applied Statistical Methods, 6, 21–29.
Zumbo, B. D., & Rupp, A. A. (2004). Responsible
modeling of measurement data for appropriate
inferences: Important advances in reliability and
validity theory. In D. Kaplan (Ed.), The SAGE
handbook of quantitative methodology for the
social sciences (pp. 73–92). Thousand Oaks, CA:
Sage Press.

Ordinal Logistic Regression
▶ Ordered Logit Model

Ordinal Measurement
▶ Multiple Scaling

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Ordinary Least-Squares (OLS) Model
Bozena Zdaniuk
Department of ECPS, University of British
Columbia, Vancouver, BC, Canada

Synonyms
General linear model; Multiple regression;
Regression

Definition
Ordinary least-squares (OLS) models assume
that the analysis is fitting a model of
a relationship between one or more explanatory
variables and a continuous or at least interval
outcome variable that minimizes the sum of
square errors, where an error is the difference
between the actual and the predicted value of
the outcome variable. The most common
analytical method that utilizes OLS models is
linear regression (with a single or multiple
predictor variables).

O
Description
Ordinary least-squares (OLS) models assume
that the analyst is fitting a model of a relationship
between one or more explanatory variables and
a continuous or at least interval outcome variable
that minimizes the sum of square errors, where an
error is the difference between the actual and
the predicted value of the outcome variable.
The most common analytical method that utilizes
OLS models is linear regression (with a single or
multiple predictor variables).
Ordinary least squares regression has been
widely used in numerous scientific disciplines
like physics, economics, and psychology, and
a multitude of text books have been written
explaining this method and its application in
different fields of research (Cohen, Cohen,

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West, & Aiken, 2003; Kleinbaum, Kupper, &
Muller, 1988; Montgomery, Peck, & Vining,
2012). In its simplest form, OLS regression
assumes linear relationship between the
x predictor and the y outcome variable and
provides solution to y ¼ a + bx + e model where
a is an intercept (value of y at x ¼ 0), b is
a regression coefficient expressing the amount
of change in y associated with one-unit change
in x, and e is a residual error – a difference
between the predicted and actual score at any
given value of x. The regression coefficient b is
of most interest because it determines the extent,
direction, and strength of the association between
y and x (Kleinbaum et al., 1988). It can be tested
for statistical significance and, if found to be
significant at a preselected p value, provides
evidence of relationship between x and y and its
magnitude. In multiple OLS regression, models
with several continuous or categorical predictors
(x1, x2, . . ., xk) are tested and regression
coefficient for each predictor is estimated while
keeping other predictors constant. Multiple OLS
regression is used to assess which of multiple
predictors are more or less important in
predicting outcome variable or how one or more
predictors relate to the outcome when controlling
for some variables known to correlate with the
outcome variable.
OLS regression is based on several assumptions which, if violated, may render the results
unreliable. The most important assumptions are
independence, homoscedasticity, and normality
of residuals. Independence of residuals assumes
that residual of one observation is independent
from residual of another observation. Violation
of independence occurs when some unmeasured
variables are systematically similar between
some groups of observations. Durbin-Watson
test can be used to detect correlated residuals.
Homoscedasticity assumes that residual variance
is the same across all predictor values. If
the assumption of homoscedasticity is violated,
we are dealing with heteroscedasticity, which can
be detected by plotting the residuals against the
predicted values or performing one of the

Ordinary Least-Squares (OLS) Model

available tests (White’s test; Breusch-Pagan
test). Normality of residuals assumes that residuals are normally distributed. This assumption
can be evaluated by plotting the residuals or
applying Shapiro-Wilk W test. In addition to
satisfying the assumptions, an important practical
concern in regression is presence of outliers
which exert undue influence on regression
results. Outliers can be univariate (a score very
different from other scores in that variable) or
multivariate (a combination of scores on several
variables very different from the same combinations for the rest of the cases) and can be detected
with graphical methods (Tabachnick & Fidell,
2001). In the case of multiple regressions, the
model also requires no multicollinearity between
the predictors. Multicollinearity is present if two
or more predictors are highly correlated and it
results in huge inflation of standard errors of the
coefficients. Multicollinearity can be diagnosed
by computing and examining variance inflation
factor (VIF).
OLS regression is widely used in quality of
life research where the quality of life measures
(usually interval) are the outcome or predictor
(e.g., Hung, Liu, Yang, & Wang, 2012; Lavallee,
Hatch, Michalos, & McKinley, 2007; Michalos &
Kahlke, 2010).
ANOVA/ANCOVA models can also be fitted
using OLS method. ANOVA compares the
means of continuous outcome variable across
the categories of one or more categorical predictors, whereas ANCOVA does the same while
controlling for a continuous covariate. ANOVA/
ANCOVA models can be considered special
cases of OLS regression.
It is crucial to remember that regression
analysis does not establish causation. The
results may indicate significant relationship
between the predictor and the outcome but do
not justify any causal inferences about that relationship. Regression-based research, however,
can be an important tool for gathering evidence
leading to establishing causal links, assuming
a number of other conditions are satisfied. An
example of a formalized list of such conditions

Organization Environment and Performance

is known as Hill’s criteria for causation and is
used widely in epidemiology research (Hill,
1971).

Cross-References
▶ Analysis of Variance
▶ Data Analysis
▶ Linear Regression Model
▶ Regression Coefficients

References
Cohen, J., Cohen, P., West, S., & Aiken, L. (2003).
Applied multiple regression/correlation analysis for
the behavioral sciences. Hillsdale, NJ: Lawrence
Erlbaum Associates.
Hill, A. B. (1971). Principles of medical statistics
(9th ed.). New York: Oxford University Press.
Hung, C., Liu, C., Yang, C., & Wang, S. (2012).
Negative impact of migraine on quality of life after
4 weeks of treatment in patients with major depressive
disorder. Psychiatry and Clinical Neurosciences, 66,
8–16.
Kleinbaum, D. G., Kupper, L. L., & Muller, K. E. (1988).
Applied regression analysis and other multivariable
methods. Belmont, CA: Duxbury Press.
Lavallee, L. F., Hatch, P. M., Michalos, A. C., &
McKinley, T. (2007). Development of the
contentment with life assessment scale (CLAS):
Using daily life experiences to verify levels of selfreported life satisfaction. Social Indicators Research,
83(2), 201–244.
Michalos, A. C., & Kahlke, M. (2010). Arts and the
perceived quality of life in British Columbia. Social
Indicators Research, 96, 1–39.
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012).
Introduction to linear regression analysis (4th ed.).
New York: Wiley.
Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. Boston, MA: Allyn and Bacon.

Organic Democracy
▶ Conceptualizing Democracy and
Nondemocracy

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Organization Environment
and Performance
Morgen Johansen1 and Rhys Andrews2
1
Public Administration Program - University of
Hawaii, Honolulu, HI, USA
2
Cardiff Business School, Cardiff University,
Cardiff, UK

Definition
Organizational environments can be crudely
characterized as “everything else” outside an
organization that may influence its behavior.
Theories of organizational environments
emphasize that the resources available within
the environment, the multiplicity of stakeholder
demands to be addressed, and the rates of change
in these each have serious implications for
organizational performance. The performance
referred to here differs in content between private,
public, and nonprofit organizations but is similar
in kind across these organizations in terms of
generic concepts of organizational effectiveness,
efficiency, and equity (Quinn & Rohrbaugh,
1981). Moreover, the performance of organizations indirectly affects the ▶ quality of life
of those within the organization and in the
organizational environment. Organizations
provide goods and services that can improve or
hinder one’s quality of life but also provide
employment opportunities and a variety of
benefits such as health insurance and
financial security.

Description
The relationship between organizational
environments and ▶ performance is a key topic
in organization studies (see Boyd & Gove, 2006).
In particular, contingency theory suggests that
the external circumstances that organizations
confront are likely to have important effects on

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organizational outcomes (Donaldson, 2001).
In fact, concern with the environment has also
been a central feature of other important
theoretical perspectives within organization studies. For example, resource-dependency theorists
(e.g., Pfeffer & Salancik, 1978) emphasize that
the impact of external sources of financial and
political support on organizations serves as
a useful supplement to the focus on managerial
perceptions of complexity and change found in
contingency theory. Likewise, although institutional theory is not necessarily well suited to
generating testable hypotheses about the relationship between organizational environments and
performance, it offers the potential for a more
nuanced evaluation of the complex regulative,
normative, and cognitive forces shaping the
choices managers make (Scott, 2001).
Among organization scientists, a strong
consensus has emerged around the validity and
applicability of Dess and Beard’s (1984) framework for analyzing organizational environments
(see Boyd & Gove, 2006). Dess and Beard (1984)
identify three key dimensions of the organizational environment: munificence, complexity,
and dynamism. Each of these dimensions is arguably a powerful influence upon managerial decision-making and thus in turn organizational
performance.
Environmental munificence (or exogenous
resource capacity) is thought to be associated
with better organizational performance, while
complexity (client homogeneity-heterogeneity
and concentration-dispersion) and dynamism
(environmental instability and turbulence) are
assumed to increase the degree of task difficulty
and so lead to worse performance (Dess & Beard,
1984). These relationships are arguably likely to
hold for both “objective” archival measures
of the environment drawn from secondary
administrative sources and “subjective” perceptual measures of the environment drawn from
surveys of practicing managers. Moreover, it is
conceivable that the effects of different
dimensions of the environment are not
straightforwardly positive or negative. The
benefits of environmental munificence may turn
negative as organizations become complacent or

Organization Environment and Performance

overconfident in their capacity to keep on doing
what they did well in the past. Likewise, at
low-medium levels, complexity and dynamism
may actually sharpen managerial awareness of
the challenges to be confronted, at least until the
environment becomes too complicated or
unpredictable to manage effectively.
The basic ideas in studying the effects
of munificence, complexity, and dynamism
on organizational performance are therefore
as follows:
A high degree of munificence is typically thought
to buffer organizations from environmental
pressures because it generates financial
and organizational slack that can, if used
effectively, facilitate both organizational
stability and growth (Cyert & March, 1963).
However, it is conceivable that organizations
can have “too much of a good thing.”
Organizational slack may be bad for
performance, on the one hand, because it
leads managers to become complacent
about the need to monitor performance
improvement effectively. This lends itself to
the broken window theory – relatively minor
problems left unattended can lead to more
serious problems in the organization and its
environment that can negatively impact
quality of life. Or, on the other hand, because
it leads them to become overconfident in
their ability to deliver more and better
services, thus:
H1: Environmental munificence exhibits
either a positive linear relationship with
organizational performance or an inverted
u-shaped one.
Environmental complexity comprises the heterogeneity and the dispersion of an organization’s
markets and services. In a heterogeneous
environment, the organization grapples with
a wide range of customers, service users, and
suppliers. In a dispersed environment, the
organization operates across a wide spread of
geographical areas. Both types of complexity
generate additional costs that need to be factored
into the production process (Aldrich, 1979).
Moreover, this complexity affects ▶ job
satisfaction (Hackman & Oldham, 1976) – an

Organization Environment and Performance

important component to quality of work life
(Warr, Cook, & Wall, 1979) – and also overall
human well-being. This is because complexity
may mean greater competition between
groups, which creates tension within the
community that affects feelings of safety and
social belonging. Even so, it is possible that
a diverse and dispersed client base may prompt
organizations to more effectively tailor the
services that they provide to clients’ requirements. Hence:
H2: Environmental complexity exhibits either
a negative linear relationship with performance or an inverted u-shaped one.
Environmental dynamism is a product of the
rate of change in external circumstances
(instability) and the unpredictability (or turbulence) of that change. Organizations typically
require greater human and financial resources
to cope effectively with turbulence and instability in the environment (Dutton, Fahey, &
Narayanan, 1983). Dynamism also affects
overall human well-being through the mental
and physical stress it can create for individuals
both in and outside the organization and also
the larger ▶ community. It is nevertheless possible that the need to demonstrate a heightened
sensitivity to the external constraints
surrounding an organization may sharpen
managerial reflexes and prompt increased
innovation and thereby generate better organizational outcomes – at least until
environmental dynamism becomes so great
as to preclude any kind of effective managerial
response. This leads to:
H3: Environmental dynamism exhibits either
a linear negative relationship with
performance or an inverted u-shaped
relationship one.
Discussion
The available evidence suggests that variations in
performance are, as expected, influenced by
measures of the environmental munificence,
complexity, and dynamism confronted by
organizations (Boyd & Gove, 2006). Organizations operating in a munificent, simple, stable,
and predictable context appear to perform better

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than their counterparts in less favorable circumstances. Indeed, each of the dimensions of the
environment identified by Dess and Beard
(1984) appears to have an important impact on
the prospects of positive organizational and
social outcomes. Even so, to date few studies
have provided a systematic analysis of the effects
of each of those dimensions within the same
study using both archival and perceptual
measures of the environment and testing for
nonlinearity in the environment-performance
relationship.
The following three lessons emerge from the
most comprehensive tests of the Dess and Beard
model to date (Andrews, 2009; Andrews &
Boyne, 2008; Andrews & Johansen, 2012): (a)
environmental munificence has an especially
strong effect on performance, (b) environmental
performance effects hold equally as strongly
for objective and subjective measures of the
environment, and (c) rather than exhibiting
a complicated nonlinear influence on
performance,
organizational
environments
seem to matter in a relatively predictable,
straightforward, and linear way.
Although the independent effects of
organizational environment on performance are
important, managerial activities are likely to
influence those effects at varying levels of
environmental munificence, complexity, and
dynamism. For example, it is quite conceivable
that facets of managerial activity will be more
or less successful at very high or very low levels
of environmental challenge. Thus, much
more work is required to analyze the full
scope of nonlinearity in the organizational
environment-performance relationship.

References
Aldrich, H. E. (1979). Organizations and environment.
Englewood Cliffs, NJ: Prentice-Hall.
Andrews, R. (2009). Organizational task environments
and performance: An empirical Analysis. International Public Management Journal, 12(1), 1–23.
Andrews, R., & Boyne, G. A. (2008). Organizational
environments and public service failure: An empirical
analysis. Environment and Planning C – Government
and Policy, 26(4), 788–807.

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Andrews, R., & Johansen, M. (2012). Organizational environments and performance: A linear or nonlinear relationship? Public Organization Review, 26(4), 788–807.
Boyd, B. K., & Gove, S. (2006). Managerial constraint:
The intersection between organizational task
environment and discretion. In D. Ketchen & D.
Bergh (Eds.), Research methodology in strategy and
management (Vol. 3, pp. 57–96). Oxford: JAI Press.
Cyert, R. M., & March, J. G. (1963). A behavioral theory
of the firm. Englewood Cliffs, NJ: Prentice-Hall.
Dess, G. G., & Beard, D. W. (1984). Dimensions of
organizational task environments. Administrative
Science Quarterly, 29(1), 52–73.
Donaldson, L. (2001). The contingency theory of organizations. Thousand Oaks, CA: Sage.
Dutton, J. M., Fahey, L., & Narayanan, V. K. (1983).
Toward understanding strategic issue diagnosis.
Strategic Management Journal, 4(4), 307–323.
Hackman, J. R., & Oldham, G. R. (1976). Motivation
through the design of work: Test of a theory.
Organizational Behavior and Human Performance,
16, 250–279.
Kelly, D., & Amburgey, T. (1991). Organizational inertia
and momentum: A dynamic model of strategic change.
Academy of Management Journal, 34(3), 591–612.
Pfeffer, J., & Salancik, G. R. (1978). The external control
of organizations. New York: Harper and Row.
Quinn, R. E., & Rohrbaugh, J. (1981). A competing values
approach to organizational effectiveness. Public
Productivity Review, 5(2), 122–140.
Scott, W. R. (2001). Institutions and organizations
(2nd ed.). London: Sage.
Warr, P., Cook, J., & Wall, T. (1979). Scales for the
measurement of some work attitudes and aspects of
psychological well being. Journal of Occupational
Psychology, 52, 129–148.

Organizational Change

Organizational Change

Definition
Organizational commitment is defined as “a psychological state that (a) characterizes the
employee’s relationship with the organization,
and (b) has implications for the decision to continue or discontinue membership in the organization” (Meyer & Allen, 1991, p. 67). This popular
definition is an attempt to create consensus
between different research traditions and definitions in the literature on organizational commitment. For example, Porter, Steers, Mowday, and
Boulian (1974) strongly focused on the affective
aspect by defining organizational commitment as
“the relative strength of an individual’s identification with and involvement in a particular organization” (p. 604). Mowday, Porter, and Steers
(1982) differentiate Porter, Steers, Mowday and
Boulian’s (1974) definition further by specifying
“identification” and “involvement” in three factors: “1. a strong belief in and acceptance of
the organization’s goals and ▶ values;
2. a willingness to exert considerable effort on
behalf of the organization and 3. a strong desire
to maintain membership in the organization”
(p. 27). These three components reflect two
research traditions in the literature of the 1970s
and 1980s, that is, attitudinal (or affective) commitment describing an emotional attachment to
the organization (Mowday, Porter, & Steers,
1982), and behavioral approaches, focusing on
the behavioral intention to remain in the organization (Allen & Meyer, 1990).

▶ Development

Description

Organizational Commitment
Karsten Mueller and Tammo Straatmann
Industrial and Organizational Psychology,
University of Osnabr€uck, Osnabr€uck, Germany

Synonyms
Involvement in organizations; Organizational
engagement; Motivation

Organizational commitment has a long history of
research interest within the industrial and organizational psychology and the study of organizational behavior (Mathieu & Zajac, 1990; Meyer,
Stanley, Herscovitch, & Topolnytsky, 2002;
Morrow, 1993; Mowday et al., 1982). This is
not surprising given the frequently reported consistent relationship of organizational commitment with other relevant attitudinal and
behavioral constructs such as ▶ job satisfaction,
▶ well-being, withdrawal intention and behavior,

Organizational Commitment

organizational citizenship behavior, and organizational ▶ productivity (Abbott, White, &
Charles, 2005; Currivan, 1999; Farkas & Tetrick,
1989; Mathieu & Zajac, 1990; Meyer & Allen,
1997; Meyer & Maltin, 2010; Meyer et al., 2002;
Porter et al., 1974). While some scholars have
discussed potential negative effects of extreme
organizational commitment or overcommitment
(e.g., Siegrist, 1996), research in most cases has
shown commitment to have positive consequences for the organization and the individual
(e.g., Mathieu & Zajac, 1990; Meyer & Maltin,
2010; Meyer et al., 2002).
The popularity of organizational commitment
resulted in a variety of different definitions and
conceptualizations of this construct. Due to these
different concepts, critique on organizational
commitment often rested on overlaps with other
constructs or unprecise definitions. Especially,
the early behavioral definitions focusing on
remaining in the organization have been criticized for mixing defining elements and consequences
of
organizational
commitment
(Swailes, 2002). In response to the critique,
there is an increasing consensus that unidimensional models focusing on the binding of an individual to the organization tend to oversimplify
a complex variable. Therefore, multidimensional
models are suggested that integrate different
aspects of organizational commitment into the
construct. For example, Meyer and Allen (1991)
differentiate between affective, continuance, and
normative commitment.
The conceptualization of affective commitment was strongly influenced by the work of
Porter and colleagues (Mowday, Steers, & Porter,
1979; Porter, Crampon, & Smith, 1976; Porter
et al., 1974). These authors described affective
commitment in terms of strong identification with
and high affective involvement in an organization (Porter et al., 1974). The emphasis of affective commitment is on the emotional attachment
to the goals and ▶ values of the organization,
reflecting an attitudinal component of organizational commitment.
Continuance commitment also referred to as
calculative commitment conceptualizes commitment as perceived earnings and costs, which are

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related to the membership in an organization.
This approach is mainly based on Becker’s
work (1960) on side bets, referring to rational
evaluations on the gains and losses of the individual associated with leaving the organization.
In this perspective, individuals are committed to
an organization because the costs of leaving outweigh the gains.
The third common type of commitment identified by Meyer and Allen (1991) among earlier
research is referred to as normative commitment,
which is seen as “the totality of normative pressures to act in a way which meets organizational
goals and interests” (Wiener, 1982, p. 421). In
this sense, organizational commitment is seen as
an obligation felt toward the organization given
by internalized ▶ norms.
The core differences between the described
dimensions of commitment are reflected by different motives underlying the attachment of
employees to the organization: “Employees with
strong affective commitment remain because
they want to, those with strong continuance commitment because they need to, and those with
strong normative commitment because they feel
they ought to do so” (Allen & Meyer, 1990, p. 3).
Allen and Meyer (1990) suggest viewing affective, continuance, and normative commitment as
components of a multidimensional construct
rather than as separated types of commitment.
Put together, the different dimensions of commitment picture the “commitment profile” (Meyer &
Herscovitch, 2001, p. 308) of an employee. Furthermore, the magnitude of the association of
organizational commitment with various outcomes is dependent on the dimension of commitment examined (Meyer et al., 2002).
The three-component model (TCM; Allen &
Meyer, 1990; Meyer & Allen, 1984, 1991) was an
attempt to unify research on attitudinal and
behavioral approaches as well as research on
three general themes in the commitment
literature.
There have been several other proposals for
a multidimensional modeling of organizational
commitment. Some models include two components (e.g., value commitment and commitment
to stay (Angle & Perry, 1981)); other models

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consist of three components (e.g., compliance,
identification, and internalization (O’Reilly &
Chatman, 1986); affective, continuance, and
moral commitment (Jaros, Jermier, Koehler, &
Sinich, 1993)). Other conceptualizations propose
other types of commitment expanding the commitment construct to different foci (CooperHakim & Viswesvaran, 2005; Reichers, 1985).
Although there is disagreement regarding the
number of constituting components of organizational commitment, there is consensus that
commitment
is
best
represented
by
a multidimensional conceptualization with varying correlates, antecedents, and consequences
depending on the considered dimension (Meyer
et al., 2002). Since the TCM introduced by Meyer
and Allen (Allen & Meyer, 1990; Meyer & Allen,
1984, 1991) has been the prevalent subject of
organizational commitment research (e.g., Felfe,
Schmook, Six, & Wieland, 2005; Herscovitch &
Meyer, 2002; Organ & Ryan, 1995), it does
not surprise that their terminology of affective,
continuance, and normative commitment prevails in commitment literature (Meyer &
Herscovitch, 2001).
In terms of antecedents of organizational commitment, Meyer et al. (2002) found in their
▶ meta-analysis generally low correlations of
organizational commitment with demographic
variables, although age and tenure showed weak
positive relationships with the three components
of commitment. Especially in the case of affective commitment, the antecedents identified in
research (e.g., job characteristics, work experiences, and structural characteristics) are under
the control of the organization through changes
in work environment features. The main antecedents of affective commitment can be summarized
as providing employees with conditions that
“contribute to employees’ ‘comfort’ in the organization [. . .] as well as their sense of ‘▶ competence’ and self-worth” (Meyer, Allen, &
Topolnytsky, 1998, p. 83). For example, management can enhance employees’ affective commitment by providing them with meaningful tasks,
teamwork (wherever applicable), decent pay, and
promotion opportunities and by granting role
clarity, especially avoiding role conflicts and

Organizational Commitment

ambiguity (Swailes, 2002; Welsch & LaVan,
1981). Moreover, work environment features
and actions expressing ▶ organizational values
related to courtesy, consideration, cooperation,
and moral integrity tend to be important for affective commitment (Finegan, 2000).
Discussion
While there seems to be an emerging consensus
about the multidimensionality, main antecedents,
and important consequences of organizational
commitment, recently there is an increased interest in connecting organizational commitment
research to established theories of social
psychology.
The social exchange theory, for instance,
interprets organizational commitment as an outcome of reciprocity and exchange mechanisms
between employer and employee (e.g.,
Eisenberger, Armeli, Rexwinkel, Lynch, &
Rhoades, 2001; Settoon, Bennett, & Liden,
1996). More specifically, organizational commitment results from the employees’ tendency “to
exchange their commitment for an employer’s
support” (Cropanzano & Mitchell, 2005;
Eisenberger, Fasolo, & Davis-LaMastro, 1990;
Eisenberger,
Huntington,
Hutchison,
&
Sowa, 1986). Accordingly, perceived organizational support has been proved as a main antecedent of organizational commitment (e.g.,
Meyer et al., 2002; Rhoades & Eisenberger,
2002). Whitener (2001) elaborates on the
exchange relationship by demonstrating that in
fact “perceived organizational support has direct
and indirect relationships, through ▶ trust-inmanagement, with organizational commitment”
(p. 530).
Other scholars discuss organizational commitment in the light of the social identification theory
(Ellemers, De Gilder, & Haslam, 2004; Haslam
et al., 2006) and describe affective commitment
as an emotional aspect of identification with the
group (Ellemers, Kortekaas & Ouwerkerk, 1999).
However, while the social exchange theory
and the social identification theory are discussed,
the specific relationship between organizational
commitment and identification still remains
unclear (Herrbach, 2006).

Organizational Commitment

According to Herrbach (2006), some authors
conceptualize organizational identification as
a component of organizational commitment
(Meyer & Allen, 1991; O’Reilly & Chatman,
1986). These authors define identification either
as “the affective dimension of commitment, or as
part of this affective dimension” (Herrbach,
2006, p. 631). Other authors emphasize
a conceptual difference between organizational
identification and organizational commitment
(Herrbach, 2006). Following van Knippenberg
and Sleebos (2006, p. 571), organizational identification reflects “the self-definitional aspect of
organizational membership,” whereas organizational commitment reflects a relationship
between two separate entities, the organization
and
the
employee.
Accordingly,
van
Knippenberg and Sleebos (2006) demonstrate
differential relationships of organizational commitment and organizational identification with
perceived organizational support, ▶ job satisfaction, and turnover intentions. Herrbach (2006)
provides additional support for the distinctiveness of both concepts by showing a relationship
of positive and negative affect with identification,
while affective commitment correlates merely
with ▶ positive affect. Other authors again conceptualize identification as an antecedent of
affective commitment (Meyer & Herscovitch,
2001).
Yet another point of view on organizational
commitment is taken by Solinger, van Olffen, and
Roe (2008), who propose a reconceptualization
of organizational commitment by integrating
actual findings into Eagly and Chaiken’s (1993)
attitude-behavior model. In this vein, affective
commitment is seen as attitude toward the target
(organization), continuance commitment is an
attitude toward a behavior (leaving or staying),
and normative commitment represents an attitude
toward normative outcomes.
Overall, the application of validated theories
in social psychology on commitment research
can be seen as a strengthening of the theoretical
background and as a new impetus for research on
organizational commitment. In addition, the
increasing interest in cross-cultural aspects
of organizational commitment is a promising

4523

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avenue for research and practice (Hausmann,
Mueller, Hattrup, & Spiess, in press).

Cross-References
▶ Attachment
▶ Competence
▶ Conceptual Framework for Quality of Life
▶ Correlation Coefficient
▶ Decision Making
▶ Demographics
▶ Exchange Theory
▶ Group Dynamics
▶ Job Satisfaction
▶ Meta-analysis
▶ Motivation
▶ Norms
▶ Organizational Values
▶ Positive Affect
▶ Productivity
▶ Rational Choice Theory
▶ Reciprocity in Exchange
▶ Role Theory
▶ Trust
▶ Well-Being at Work

O
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Currivan, D. B. (1999). The causal order of job satisfaction and organizational commitment in models of

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4524

employee turnover. Human Resource Management
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Eisenberger, R., Armeli, S., Rexwinkel, B., Lynch, P. D.,
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Eisenberger, R., Huntington, R., Hutchison, D., & Sowa,
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Organizational Commitment
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Organizational Values
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Definition
Organizational values can be defined as beliefs
about socially or personally desirable end
states or actions that are explicitly or implicitly
shared by members of an organization (e.g.,
Schwartz, 1992). Hence, organizational values
function as guides to what is seen as good and
important in the organization (VandenBos, 2007;
Williams, 1970). Moreover, they are influencing
the selection and evaluation of decisions and
actions in an organization (Kluckhohn, 1951;
Rokeach, 1973; Schwartz, 1992).
In line with the general conceptualization of
values, organizational values transcend single
situations and are relatively stable over time
(Schwartz, 1999). While the sharedness or agreement in the organization is a key defining
characteristic of organizational values (Rousseau, 1990), the existence of subcultures in
an organization is also possible (e.g., van den
Berg & Wilderom, 2004).

Description

Organizational Culture
▶ Organizational Values

Organizational Engagement
▶ Organizational Commitment

Organizational Values
Karsten Mueller and Tammo Straatmann
Industrial and Organizational Psychology,
University of Osnabr€uck, Osnabr€uck, Germany

Synonyms
Organizational culture

The conceptualization of organizational values
differs to some degree among researchers
(Wiener, 1988). According to Wiener (1988),
one source of these inconsistencies is a certain
confusion of organizational values with other
constructs like ▶ norms, goals, or attitudes. However, many scholars suppose that values are more
global and abstract in nature and become manifest
in ▶ norms, goals, attitudes, decisions, and actions
(e.g., Beyer, 1981; Kluckhohn, 1951; Rokeach,
1973; Schmidt & Posner, 1986; van der Wal &
Huberts, 2008).
In addition, organizational values are seen as
the core element of an organizational culture
(e.g., Hofstede, 2001; Wiener, 1988). Consequently, research on organizational culture is
often based on the assessment of organizational
values (e.g., van den Berg & Wilderom,
2004). The reference to established organizational value frameworks in research on
organizational culture allows for a detailed specification of research questions (Child, 1981;

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4526

House, Wright, & Aditya, 1997). The reference to
specific values avoids treating organizational culture as an omnibus construct explaining any differences found between organizations as being of
cultural nature. Otherwise, a cultural explanation
turns out to be an “empty statement” (van de
Vijver & Leung, 2000, p. 36) because it only
paraphrases the existence of found differences
without any additional explanation (Georgas,
van de Vijver, & Berry, 2004).
Organizational Values and Their
Consequences and Antecedents
Organizational culture and its related values are
thought of as the “glue” which holds the organization together and stimulates commitment and
▶ performance of the employees (van den
Berg & Wilderom, 2004, p. 571). Likewise, organizational culture is suggested to be a key factor
in influencing organizational effectiveness
(Denison & Mishra, 1995; Wilkins & Ouchi,
1983) and thus can be a main source of competitive advantage for organizations (Barney, 1986).
In the same vein, literature and research
concerning organizational values and personorganization fit have been linked to many important work-related constructs, such as ▶ motivation (e.g., Locke, 1991), ▶ satisfaction (e.g.,
Ashforth & Mael, 1989), commitment (Meyer,
Irving, & Allen, 1998), ▶ performance (e.g.,
Hartnell, Ou, & Kinicki, 2011), or turnover
(O’Reilly, Chatman, & Caldwell, 1991). Specifically, Hartnell et al.’s (2011) ▶ meta-analysis on
the effect of organizational culture using the
competing values framework (CVF; Quinn &
Rohrbaugh, 1983) showed significant relationships of organizational values with important
▶ indices of organizational ▶ performance.
Their results indicate that variations in value profiles of an organization demonstrate a differential
relationship to employee attitudes (e.g., ▶ Job
Satisfaction, ▶ Organizational Commitment),
organizational effectiveness (e.g., innovation,
quality of products and services), and financial
performance (e.g., subjective and objective
profit or growth).
According to Schein, cultural elements like
values are based on “basic assumptions that

Organizational Values

a given group has invented, discovered, or
developed in learning to cope with its problems
of external ▶ adaptation and internal integration,
and that have worked well enough to be considered valid” (Schein, 1984, p. 3). Thus,
a prerequisite for the development of organizational values is a shared history of problem
solving among a definable group of people in
the organization (Schein, 1984).
Initially, the specific organizational values are
formed as a result of the actions and decisions of
the organization’s leaders during the development of the organization. Especially the
founders of an organization are very influential
in forming organizational values (e.g., Schein,
1984; Schneider, 1987; Siehl, 1985). Yet, the
formation of organizational values will ultimately be a result of shared experiences and
interaction in the organization (Schein, 1984).
Specifically, leadership in an organization will
always play an important role in the development
and management of organizational values.
Organizational Values and Organizational
Dynamics
Organizational cultures and specifically organizational values are built around functional
and successful problem solving in the specific
organization. The successful organizational values
that form the organizational culture are carried on
and are reflected in and by organizational strategies, regulations, ▶ norms, and goals (e.g., Wiener,
1988, p. 536). Moreover, organizational values
become manifested in organizational artifacts like
organizational rituals (e.g., Deal & Kennedy,
1982), symbols (e.g., Turner, 1990), and stories
(e.g., Mitroff & Kilmann, 1976). Thus, organizational values influence employees’ everyday
behavior as they are directly and indirectly
reflected in the customs, systems, ▶ norms, and
practices of the organization (Bourdieu, 1972;
Markus & Kitayama, 1994; Schwartz, 1999, p. 25).
These institutionalized and crystallized
organizational values are what make changing an
organizational culture a true challenge (Hofstede,
1980), as the systems and processes often transport
the old values and also have to be changed to
support the aspired new organizational values.

Organizational Values

With reference to Schein’s (1984) theory,
Hatch (1993) developed a process model of
organizational cultures. In order to incorporate
the dynamic of organizational cultures, her
cultural dynamics model brings together ideas
from functionalistic and symbolic perspectives
to explain the processes of manifestation,
realization, symbolization, and interpretation of
organizational culture (Hatch, 1993). According
to the model’s basic assumption, values, artifacts,
and symbols are all connected in dynamic
relationships continuously producing and
reproducing the organizational culture.
From a practical perspective, one major process of the culture production and reproduction
based on organizational values is the organizational selection and socialization (Hofstede,
1980; Schneider, 1987; Schneider, Goldstein, &
Smith, 1995; Wiener, 1988). While the selection
mainly refers to the recruitment and promotion
of employees based on organizational values,
the socialization covers all aspects and processes
of transmitting the organizational values to the
employees (Etzioni, 1961). Yet, both selection
and socialization are in many cases not systematically planned and intentionally performed
acts but strongly influenced by organizational
values. The awareness of these processes was
heightened by Schneider’s seminal work on
the attraction-selection-attrition (ASA) model
(Schneider, 1987; Schneider et al., 1995). The
ASA model (Schneider, 1987; Schneider et al.,
1995) proposes a multiple-step perspective on
how new members are brought in line with the
organizational values. Schneider’s first assumption is that people are more likely to apply for
a position in the specific organization when their
value structure fits the organizational values.
In a second step, the organization will likewise
select and promote people who seem to fit better
to the organizational values. Thus, employees
with a better person-organization fit will be
retained in the organization.
While the ASA model has been very influential in research and literature, one major point of
criticism is that applicants often do not know
the organizational values of the respective company (Billsberry, 2004). Today, however, many

4527

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companies actively engage in employer branding
and explicitly promote their organizational
values and benefits.
Measurement of Organizational Values
In terms of measuring organizational values, different approaches and methods have been developed. At a very basic level, a qualitative approach
and a quantitative approach can be distinguished.
The qualitative approach tries to extract the organizational values from organizational documents,
interviews, and observations (Schein, 1984).
Analyzing the content of organizational documents is supposed to have a great potential for
research on organizational values (Martin &
Siehl, 1983; Rousseau, 1990; Daly, Pouder, &
Kabanoff, 2004). When applying interviews, the
interview partners (e.g., founders, upper management, instructors, supervisors, mentors) are often
chosen because of their prominent role in the
development and transmission of organizational
culture (Schein, 1984).
The same is true for the quantitative approach,
in which organizational leaders or socialization
agents are often chosen as participants of organizational value surveys. However, due to the specific advantages of surveys, it is easy to extend the
surveys to a high number of organizational members. Thus, surveying employees delivers a broad
picture of organizational values and may even
uncover certain subcultures or levels of culture
formation in the organization (Schein, 1984).
As importance is a key characteristic of the
value conceptualization (VandenBos, 2007),
research is concerned about how to operationalize
value importance. Direct ratings of value importance represent the most common approach in
research on values (Hattrup, Mueller, & Aguirre,
2007). However, it has been suggested that indirect
approaches (e.g., inferring the importance from the
strength of the relationship with criterion variables) may be less susceptible to biases such as
social desirability and may be more adequate
to assess values that exist at a subconscious
level (e.g., Hattrup et al., 2007; Hofstede, 2001;
Mumford, Scott, Gaddis, & Strange, 2002).
Overall, many scholars suggest to derive
organizational value profiles by aggregating

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4528

individual ratings (e.g., Hofstede, 1980; Inkeles
& Smith, 1974; Schwartz, 1999). This suggestion
is based on the idea that individual ratings are
influenced by shared cultural experiences
and their enculturation, as well as by underlying
organizational values (Schwartz, 1999).
While Likert-scaled items are recommended
for the practical value assessment (Billsberry,
2004), research disagrees with regard to the content of the value ▶ survey. Today, there are
many different value dimensions proposed for
research in organizations (see Ashkanasy,
Broadfoot, & Falkus, 2000). The number of
scales assessing organizational values increases
even more, as many organizational culture scales
primarily focus on the measurement of values
(van den Berg & Wilderom, 2004). Among the
most established scales measuring organizational
values are Rokeach’s List of Values (1973),
Schwartz’s Value Survey (1992), the Competing
Values Framework by Quinn and Rohrbaugh
(1983), Denison’s Organizational Culture Survey
(Denison & Neale, 1996), and the Organizational
Culture Profile developed by O’Reilly et al.
(1991) and revised by Sarros, Gray, Densten,
and Cooper (2005). Based on the multitude of
measured dimensions, there are some efforts to
integrate the existing concepts and identify basic
universal dimensions (e.g., Detert, Schroeder, &
Mauriel, 2000; van den Berg & Wilderom, 2004).
For example, van den Berg and Wilderom (2004)
identified five basic dimensions of organizational
culture (namely, autonomy, external orientation,
interdepartmental coordination, human resource
orientation, and improvement orientation) after
a thorough review of the existing literature on
organizational value dimensions and their own
research. However, many of the current scales
and frameworks are missing a sound theoretical
foundation (see, e.g., Allaire & Firsirotu, 1984;
Hartnell et al., 2011). Accordingly, critiques
point out that the existing lists of values often
only partly apply to a specific organization (van
Rekom, van Riel, & Wierenga, 2006). As a result,
some scholars suggest that the dimensions and
items should be developed to suit the organizational context (Schwartz, 1994). Others propose
a two-step approach: using qualitative interviews

Organizational Values

in a first step and surveying the employees based
on these findings. Yet, due to the ad hoc development, this will lead to results that are hardly
comparable between different organizations (van
Rekom et al., 2006). Thus, there is still an urgent
need for a theoretically based integration and
convergence of the organizational culture dimensions to be generalizable across different organizations (e.g., van den Berg & Wilderom, 2004).
Discussion
According to the early value theorists and
researchers, values offer a powerful perspective
in studying organizations, decisions, and actions
(e.g., Rokeach, 1973; Schein, 1984). Therefore,
organizational values are seen as promising and
influential management instruments (Pruzan,
1998). Moreover, organizational values have
been found to influence organizational ▶ performance and employee attitudes (e.g., Ashforth &
Mael, 1989; Hartnell et al., 2011; Meyer et al.,
1998). Furthermore, they are supposed to affect
organizational processes like selection, socialization, and retention (Schneider, 1987; Schneider
et al., 1995). Hence, more and more organizations
are concerned with the management of their organizational values in terms of a value-based management perspective (e.g., Anderson, 1997).
Especially in times of ambiguity and uncertainty,
scholars suggest to employ management by values
instead of bureaucratic mechanisms because they
allow for a more flexible and adaptive management of the organization (Ouchi, 1979).
Overall, it is thus obvious that the understanding of organizational values will be highly relevant for practitioners and may “become integral
to the process of management itself” (Schein,
1984, p. 14). However, concerning the understanding of organizational values and organizational culture, there is still a general need for
more theoretically driven and interdisciplinary
research approaches (Allaire & Firsirotu, 1984)
combining the ▶ knowledge and perspectives of
▶ anthropology, sociology (e.g., Parsons, 1951),
psychology (e.g., Schwartz, 1992), and management (e.g., Schein, 1984). Such an interdisciplinary perspective and integration has the
potential to give new impetus to the research on

Organizational Values

the development, functions, and dynamics of
organizational values in order to improve organizational effectiveness as well as individual and
organizational ▶ well-being.

Cross-References
▶ Adaptation
▶ Aggregation Problem
▶ Anthropology
▶ Competitiveness
▶ Conceptual Framework for Quality of Life
▶ Criterion Validity
▶ Decision Making
▶ Generalizability Theory
▶ Group Dynamics
▶ Human Capital
▶ Importance Rating(s)
▶ Indices
▶ Inference, Statistical
▶ Job Satisfaction
▶ Knowledge Transfer and Exchange
▶ Levels of Measurement
▶ Likert Scale
▶ Measurement Methods
▶ Meta-analysis
▶ Motivation
▶ Norms
▶ Organizational Commitment
▶ Performance Indicators
▶ Qualitative Methods
▶ Social Desirability Bias
▶ Storytelling
▶ Survey Administration
▶ Survey Research
▶ Symbolic Capital
▶ Validity, Logical
▶ Well-Being at Work

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Organizations, Quality of Life in
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Organizations, Quality of Life in
Guowei Jian
School of Communication, Cleveland State
University, Cleveland, OH, USA

Synonyms
Quality of working life

Definition
Employee well-being in organizations.

Description
Research on quality of life in organizations
saw its early development take place in North
America and Europe in the 1970s and 1980s.
Known as quality of working life (QWL), it refers
to employee well-being at work. Work programs
of QWL aimed at humanization at work and
improvement of ▶ employee well-being through
job and organizational redesign (Jenkins, 1983).
Although QWL is associated with a broad range
of theories, such as job enrichment theory and
theory of socio-technical systems, satisfaction of
employee needs at various levels is central to
the construct of QWL and its measurement
(Sirgy, Efraty, Siegel, & Lee, 2001).
As researchers gradually recognize the complex and multifaceted nature of QWL, multiple
areas of research, previously considered
dimensions of QWL, have grown into more

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specialized programs of studies. These areas
include, although not limited to, ▶ work stress,
work-family balance, and organizational support.
Work stress, understood as job demand
experienced by employees, has been linked to
several serious health problems, such as
significant weight gain (Block, He, Zaslavsky,
Ding, & Ayanian, 2009) and coronary heart
disease (Aboa-Eboulé et al., 2007). Role ambiguity, role conflict, and role overload are three
significant and commonly recognized job
stressors (Kahn, Wolfe, Quinn, & Snoek, 1964).
Drawing upon role dynamic theory, role stressors
emerge as organizational members perceive
certain psychological forces when interpreting
role-related information in the form of influence
attempts by others (Kahn et al., 1964). In particular, research found that leader behaviors and
leader-employee relationships were associated
with employee stress and affective well-being
(Skakon, Nielsen, Borg, & Guzman, 2010).
Role stressors have been found to be negatively
related to job performance (Gilboa, Shirom,
Fried, & Cooper, 2008). Prolonged exposure to
stressors could result in burnout, which
takes multiple forms, such as emotional
exhaustion and feeling a lack of personal
accomplishment (Maslach, Schaufeli, & Leiter,
2001). The outcome of ▶ burnout could be
both attitudinal and physiological. For example,
evidence has linked burnout to lowered ▶ job
satisfaction (Miller, Ellis, Zook, & Lyles, 1990)
and increased risk of cardiovascular disease
(Shirom, Toker, Berliner, & Shapira, 2006).
Originally conceptualized within the literature
of work stress, work-family balance has seen
tremendous growth in itself in recent years
(Brough & Kalliath, 2009). This stream of
research recognizes that understanding quality
of life in organizations cannot be separated from
an understanding of quality of life in nonwork
domains. Work-family imbalance refers to stress
resulted from work-family conflict and stress
spilled over from work to nonwork domains and
vice versa. Work-life imbalance takes place not
only at the individual level across domains but
also at the dyadic and group level, for instance,
between spouses at home, which is referred to as

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a crossover effect due to transmission of
emotions (Westman, Brough & Kalliath, 2009).
More specifically, two types of ▶ work-family
conflict exist: work-to-family conflict (WFC)
and family-to-work conflict (FWC). WFC refers
to the extent to which family life is made difficult
as a result of work roles, and the reverse is FWC
(Michel, Kotrba, Mitchelson, Clark, & Baltes,
2011). Role stressors at work and at home
contribute to WFC and FWC, respectively.
In general, work-family conflict is not only associated with adverse self-reported health outcomes
but also objective health indictors including cholesterol level, body mass index, and physical
stamina (van Steenbergen & Ellemers, 2009).
By contrast, employees in organizations that
facilitate work-family balance achieve better
health outcomes. Recent development in communication technologies affords more flexible work
arrangement in many industries with the aim of
facilitating work-family balance. An increasing
number of organizations implement telework,
also known as telecommuting, programs as part
of their flexible work arrangements, allowing
employees to work from home on a part-time or
full-time basis. Recent meta-analysis confirms
the beneficial effects of telework on lowering
work-family conflict and increasing job satisfaction (Gajendran & Harrison, 2007).
To compete for qualified employees and
improve performance, organizations invest
resources to help employees relieve work stress
and facilitate work-family balance. Perceived
organizational support (POS) as a construct refers
to employees’ perception with regard to the
extent to which an organization cares about
their well-being (Eisenberger, Huntington,
Hutchinson, & Sowa, 1986). Specifically, three
categories of treatment are significant
predictors of POS, including fairness of treatment, supervisor support, and organizational
rewards and favorable job conditions (Rhoades
& Eisenberger, 2002). Two decades of research
has demonstrated that POS have a strong, positive association with organizational commitment
and job satisfaction and negative association
with employees’ intent to leave (Riggle,
Edmondson, & Hansen, 2009).

Organizations, Quality of Life in

In summary, research on quality of life
in organizations in the past few decades has
produced rather conclusive results regarding the
damaging effects of unfavorable work conditions
and stress on ▶ employee well-being and the
beneficial effects of organizational support on
employee
attitudinal
and
psychological
outcomes.

Cross-References
▶ Organizational Commitment
▶ Organizational Values

References
Aboa-Eboulé, C., Brisson, C., Maunsell, E., Mâsse, B.,
Bourbonnais, R., Vézina, M., et al. (2007). Job strain
and risk of acute recurrent coronary heart disease
events. JAMA: The Journal of the American Medical
Association, 298, 1652–1660.
Block, J. P., He, Y., Zaslavsky, A. M., Ding, L., &
Ayanian, J. Z. (2009). Psychological stress and change
in weight among U.S. adults. American Journal of
Epidemiology, 170(2), 181–192.
Brough, P., & Kalliath, T. (2009). Work-family balance:
Theoretical and empirical advancements. Journal of
Organizational Behavior, 30, 581–585.
Eisenberger, R., Huntington, R., Hutchinson, S., &
Sowa, D. (1986). Perceived organizational support.
The Journal of Applied Psychology, 71, 500–507.
Gajendran, R., & Harrison, D. (2007). The good, the bad,
and the unknown about telecommuting: Meta-analysis
of psychological mediators and individual consequences. The Journal of Applied Psychology, 92,
1524–1541.
Gilboa, S., Shirom, A., Fried, Y., & Cooper, C. (2008). A
meta-analysis of work demand stressors and job performance: Examining main and moderating effects.
Personnel Psychology, 61, 227–271.
Jenkins, D. (1983). Quality of working life: Trends and
directions. In H. Kolodny & H. van Beinum (Eds.),
The quality of working life and the 1980s (pp. 1–32).
New York: Praeger.
Kahn, R. L., Wolfe, D. M., Quinn, R. P., & Snoek, J. D.
(1964). Organizational stress: Studies in role conflict
and ambiguity. New York: Wiley.
Maslach, C., Schaufeli, W. B., & Leiter, M. P. (2001). Job
burnout. Annual Review of Psychology, 52, 397–422.
Michel, J., Kotrba, L., Mitchelson, J., Clark, M., & Baltes, B.
(2011). Antecedents of work-family conflict: A metaanalytic review. Journal of Organizational Behavior, 32,
689–725.

Oswestry Disability Questionnaire
Miller, K., Ellis, B., Zook, E., & Lyles, J. (1990).
An integrated model of communication, stress, and
burnout in the workplace. Communication Research,
17, 300–326.
Rhoades, L., & Eisenberger, R. (2002). Perceived
organizational support: A review of the literature.
The Journal of Applied Psychology, 87, 698–714.
Riggle, R., Edmondson, D., & Hansen, J. (2009). A
meta-analysis of the relationship between perceived
organizational support and job outcomes: 20 years of
research. Journal of Business Research, 62,
1027–1030.
Shirom, A., Toker, S., Berliner, S., & Shapira, I. (2006).
Burnout and risk of cardiovascular disease: Evidence,
possible causal paths, and promising research
directions. Psychological Bulletin, 132, 327–353.
Sirgy, M., Efraty, D., Siegel, P., & Lee, D. (2001). A new
measure of quality of work life (QWL) based on need
satisfaction and spillover theories. Social Indicators
Research, 55, 241–302.
Skakon, J., Nielsen, K., Borg, V., & Guzman, J. (2010).
Are leaders’ well-being, behaviors and style associated
with the affective well-being of their employees?
A systematic review of three decades of research.
Work and Stress, 24, 107–139.
van Steenbergen, E. F., & Ellemers, N. (2009). Is managing the work-family interface worthwhile? Benefits for
employee health and performance. Journal of Organizational Behavior, 30, 617–642.
Westman, M., Brough, P., & Kalliath, T. (2009). Expert
commentary on work-life balance and crossover of
emotions and experiences: Theoretical and practice
advancements. Journal of Organizational Behavior,
30, 587–595.

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OSIS
▶ Oxford Shoulder Instability Score

OSS
▶ Oxford Shoulder Score

Ostracism
▶ Disadvantaged Populations

Oswestry Disability Index
▶ Oswestry Disability Questionnaire

O
Oswestry Disability Questionnaire

Orgasm
▶ Sexual Functioning

Megan Davidson
School of Physiotherapy, La Trobe University,
Bundoora, VIC, Australia

Synonyms

Orientations to Happiness
▶ Pleasure, Engagement, Meaning, and
Happiness

Oswestry disability index; Oswestry low back
disability questionnaire

Definition

OSIQ
▶ Oxford Shoulder Instability Score

The Oswestry Low Back Pain Disability
Questionnaire is a 10-section, self-report questionnaire to evaluate the impact of back pain on
activities.

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4534

Description
The Oswestry Low Back Pain Disability Questionnaire (Fairbank, Couper, Davies & O’Brien,
1980) was developed in the late 1970s and has
been widely used. It is now often called the
Oswestry Disability Index (ODI). Ten sections
cover Pain, Personal Care, Lifting, Walking,
Sitting, Standing, Sleeping, Sex Life, Social
Life, and Traveling. Each section has six statements reflecting an increasing level of disability.
Respondents choose the statement that most
applies to them in each section. The first statement is scored 0; the second is scored 1 and so on
to 5 for the last statement. The sum of the section
scores is divided by 50 if all sections are completed and multiplied by 100 for the final percentage score. The denominator is adjusted for
missed sections (e.g., the sum of the section
scores is divided by 45 if one section is not
completed). It takes 5 min for the patient to
complete the Oswestry and less than 1 min
to score. The total possible score ranges from
0 to 100, and a higher score indicates worse
function. Scores from 0 % to 20 % are categorized as “minimal disability,” 20–40 % “moderate disability,” 40–60 % “severe disability,”
60–80 % “crippled,” and from 80 % to 100 %
“bedbound or exaggerating” (Fairbank et al.,
1980). The term “housebound” is a more appropriate contemporary descriptor for the 60–80 %
score range.
A number of modified versions of the
Oswestry have been published. Baker, Pynsent,
and Fairbank (1989) removed the references to
analgesic medication in the Pain section and
to tablets in the Sleeping section. Version 2.0
(Fairbank & Pynsent, 2000) retained the Baker
et al. (1989) modifications, and changed the
instruction to answer the questions in relation to
how their back problem is affecting them
“today,” rather than the original instructions
which do not specify a time frame. A version
adapted by the American Academy of Orthopaedic Surgeons (AAOS) omits the Pain, Sex Life,
and Social Life sections and uses time rather than
distance in the Walking section (Deyo et al.,
1998). It is also differently scored and asks

Oswestry Disability Questionnaire

respondents about how pain has affected their
activities “in the past week” (Fairbank &
Pynsent, 2000).
Three modified Oswestry versions have been
published that replace the “Sex Life” section,
which is frequently not completed by respondents, with an alternative section. Hudson-Cook,
Tomes-Nicholson, and Breen (1989) replaced the
“Sex Life” section with one called “Changing
Degree of Pain.” As a transition question
(i.e., how much has pain intensity changed over
time), this is structured differently to the other
sections and has been confirmed as not fitting
with the other items (Davidson, 2008). This version is also called the Chiropractic revised
version. A comparison of the wording of version
1.0, 2.0, AAOS, and Chiropractic versions can be
found in Fairbank and Pynsent (2000).
Fritz and Irrgang (2001) in the USA replaced
“Sex Life” with “Employment/Homemaking,”
and Davidson (2008) retained this section but
changed the wording to “Work/Housework”
for the Australian setting. Davidson and Keating
(2002) also changed the imperial units
(yards/miles) in the Walking section to metric
distances (meters/kilometers) and modified the
final statement in the section from “I am in bed
most of the time and have to crawl to the toilet” to
“I am unable to walk at all.”
Versions of the Oswestry have been reported
in Brazilian-Portuguese (Vigatto, Alexandre &
Filho, 2007), Chinese (Lue, Hsieh, Huang, Lin &
Lu, 2008), Danish (Lauridsen, Hartvigsen,
Manniche, Korsholm & Grunnet-Nilsson,
2006a,
2006b),
Finnish
(Pekkanen,
Kautiainen, Ylinen, Salo & H€akkinen, 2011),
German (Mannion, Junge, Fairbank, Dvorak &
Grob, 2006; Osthus, Cziske & Jacobi, 2006),
Greek (Boscainos, Sapkas, Stilianessi &
Prouskas, 2003), Italian (Monticone et al.,
2009), Iranian (Mousavi, Parnianpour, Mehdian,
Montazeri & Mobini, 2006), Korean (Kim et al.,
2005), Norwegian (Grotle, Brox & Vøllestad,
2003), and Turkish (Yakut et al., 2004).
The previously free-domain use of the
Oswestry has recently been curtailed, with
a licensing agreement and royalty payments
now required for funded research but free use

Oswestry Disability Questionnaire

for unfunded research and clinical practice.
Details can be found at the MAPI Research
Trust website www.mapi-trust.org.
Reliability and Measurement Error
Test-retest reliability coefficients of r ¼ 0.94 and
0.89 for same day retest have been reported by
Triano, McGregor, Cramer, and Emde (1993) and
Baker et al. (1989), respectively. Longer retest
periods have used subgroups of participants who
self-rated their condition as unchanged and coefficients (ICC) reported of 0.83 (Gronblad et al.,
1993), 0.84 (95 % CI 0.73, 0.91) (Davidson &
Keating, 2002), 0.90 (95 % CI 0.78, 0.96)
(Fritz & Irrgang, 2001), and 0.94 (Kopec et al.,
1995). Davidson and Keating (2002) reported
a standard error of measurement of 6 points,
with error around repeated measures of 9, and
a minimum detectable change at 90 % confidence
(MDC90) of 10–15 points for a version with metric distances in the Walking section. Fritz and
Irrgang (2001) reported SEM of 5.4 and MDC90
of 12.68 for the version with “Employment/
Homemaking” replacing “Sex Life.” Mannion
et al. (2006) reported the MDC95 as around
9 points for the German version. The MDC is
the magnitude of change score for a stated level
of confidence that the change is greater than
measurement error.
Validity and Minimum Clinically Important
Difference
There is considerable evidence to support the
construct validity of the Oswestry Disability
with most of the evidence from studies of convergent validity that demonstrate an expected
relationship between measures of the same or
related constructs (e.g., Gronblad et al., 1993;
Gronblad, Jarvinen, Hurri, Hupli & Karaharju,
1994; Kopec et al., 1995). Evidence of validity
has also been provided by studies of groups with
expected differences (▶ known-groups validity)
(e.g., Bolton & Christensen, 1994; Leclaire,
Blier, Fortin & Proulx, 1997).
Page, Shawaryn, Cernich, and Linacre (2002)
analyzed data for 95 respondents using Rasch
analysis and proposed that item 1 (Pain section
with reference to analgesic medication) measured

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a different dimension to the other items and that
there were disordered response thresholds
(i.e., the 6-level responses not being used as
expected). A 9-section Oswestry, with response
options collapsed to 4 levels, was required to
achieve adequate fit of the data to the Rasch
model. Personal care was the easiest activity,
and lifting was the most difficult.
Davidson (2008) used Rasch analysis to analyze data from 100 respondents who completed
a 12-section Oswestry to allow comparison of
three versions: the standard 10-section Oswestry,
the version with Work/Housework replacing Sex
Life, and the version with Change in Pain
replacing Sex Life. The Walking section used
metric rather than imperial distances. In all
three versions, sleeping and personal care were
the easiest activities, and lifting was the most
difficult. The only misfitting item was the Changing Degree of Pain item. The Walking section
showed uniform differential item functioning by
age, with respondents in the older age category
returning a higher score than predicted. Some
disordered thresholds were observed in the Social
Life, Standing, Personal Care, and Sex Life
sections.
Using the best trade-off between sensitivity
and specificity on the ROC curve for change
score that correctly identifies changed and stable
persons, the minimum clinically important difference (MCID) has been reported as 6 points for the
version
with
“Employment/Homemaking”
(Fritz & Irrgang, 2001), 4.45 points for the
Brazilian-Portuguese version (Coelho et al.,
2008), 5 points for the Norwegian version (Grotle
et al., 2004), and 9 points for the Danish version
(Lauridsen, Hartvigsen, Manniche, Korsholm &
Grunnet-Nilsson, 2006b). The MCID is the
smallest amount of change that would be considered clinically important.
Head to Head Comparisons with Other Scales
A Canadian study reported similar reliability and
responsiveness of the Oswestry, the Quebec, and
the Roland-Morris scales, with all these superior
to the SF-36 Physical Functioning scale
(Kopec et al., 1995). An Australian study
reported the Oswestry (with modified Walking

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section), Quebec, and SF-36 Physical Functioning scales had superior reliability compared to the
Roland-Morris and Waddell scales, but all scales
had similar responsiveness (Davidson & Keating,
2002). A UK study found the Oswestry
(v2.1) was overall more responsive than the
Roland-Morris or a patient-specific questionnaire
(Frost et al., 2008). Grotle et al. (2004) reported
similar responsiveness of the Norwegian version
of the Oswestry compared to the Roland-Morris,
Disability Rating Index, and SF-36 Physical
Functioning scale.
Summary
The Oswestry is easy for both patients and clinicians to use, and its psychometric properties are
well established. A change in total score of
around 10 points allows one to be 90 % confident
that the observed change is beyond measurement
error. Change of less than 10 points should not be
entirely discounted; however, the smaller the
change in score, the greater the likelihood that
variation in scores is due to measurement error.
When the initial score is less than 10 points, there
is insufficient range remaining to detect improvement in scores beyond measurement error
(at 90 % confidence), and another instrument
should be selected for patient assessment.
A change of between 5 and 9 points is considered
clinically important. For individual patients,
the change in section scores as well as total
scores should be inspected to determine which
activities are improving. A very high score
(>80 %) in ambulatory patients is rare, and clinicians should explore personal and environmental factors that may distort a person’s selfreporting of disability.
Strengths
• Well-established psychometric properties.
• Translations in many languages are available.

Limitations
• Version 2.1a uses imperial distance units
(yards/miles) in the Walking section, limiting
the usefulness of this English-language

Oswestry Disability Questionnaire

version to countries that retain an imperial
measurement system.
• The need to register to use the currently
approved version creates a barrier to use.
• High nonresponse rates to the “Sex Life”
section.
• The pain-focused language may be counterproductive in chronic pain management when
redirecting patient attention away from pain is
an important treatment goal.

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17, 1101–1106
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Deyo, R. A., Battie, M., Beurskens, A. J., Bombardier, C.,
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Fairbank, J. C., & Pynsent, P. B. (2000). The Oswestry
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Out of Work
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Grotle, M., Brox, J. I., & Vøllestad, N. K. (2003).
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Grotle, M., Brox, J. I., & Vøllestad, N. K. (2004).
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Hudson-Cook, N., Tomes-Nicholson, K., & Breen, A.
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Kim, D.-Y., Lee, S.-H., Lee, H.-Y., Lee, H.-J., Chang,
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Lauridsen, H. H., Hartvigsen, J., Manniche, C., Korsholm,
L., & Grunnet-Nilsson, N. (2006b). Danish version of
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Mannion, A. F., Junge, A., Fairbank, J. C., Dvorak, J., &
Grob, D. (2006). Development of a German version of
the Oswestry disability index. Part 1: Cross-cultural
adaptation, reliability, and validity. European Spine
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Pillastrini, P., et al. (2009). Development of the Italian
version of the Oswestry disability index (ODI-I). Spine,
34, 2090–2095.
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index, the Roland-Morris disability questionnaire,
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and validation studies of the Iranian versions. Spine,
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Osthus, H., Cziske, R., & Jacobi, E. (2006). Cross-cultural
adaptation of a German version of the Oswestry
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Pekkanen, L., Kautiainen, H., Ylinen, J., Salo, P., &
H€akkinen, A. (2011). Reliability and validity study of
the finish version 2.0 of the Oswestry disability index.
Spine, 36, 332–338.
Triano, J. J., McGregor, M., Cramer, G. D., & Emde, D. L.
(1993). A comparison of outcome measures for use
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Vigatto, R., Alexandre, N. M. C., & Filho, H. R. C.
(2007). Development of a Brazilian Portuguese version of the Oswestry disability index. Spine, 32,
481–486.
€
Yakut, E., D€
uger, T., Öks€
uz, Ç., Yör€
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version of the Oswestry disability index for patients
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Oswestry Low Back Disability
Questionnaire
▶ Oswestry Disability Questionnaire

Out of Work
▶ Unemployment

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Outcome Assessment, Quality of Life

Description

Outcome Assessment, Quality of Life
▶ Changes in Quality of Life

Outcomes and Indicators:
Dayton-Montgomery County
Robert L. Stoughton
Fitz Center for Leadership in Community,
University of Dayton, Dayton, OH, USA

Synonyms
Community indicators; Montgomery county
indicators

Definition
“Outcomes and indicators” is the term used in
Montgomery County, Ohio, to describe its use
of Results-Based AccountabilityTM (RBATM).
RBATM is “a common sense approach that . . .
can be used to improve the quality of life in
communities, cities, counties, states and nations,
including everything from the well-being of children to the creation of a sustainable environment”
(Friedman, 2005). The evolution of Montgomery
County’s use of RBATM is described in a series of
annual reports published by the Montgomery
County Family and Children First Council. The
outcomes are conditions of well-being to which
the community aspires; the Family and Children
First Council (FCFC) has articulated six outcomes: Healthy People, Young People
Succeeding, Stable Families, Positive Living for
Special Populations, Safe and Supportive Neighborhoods, and Economic Self-Sufficiency. Indicators are quantifiable measures that can be
attached to the outcomes. The FCFC is currently
tracking 27 indicators distributed among the six
outcomes.

Over the past few decades, many communities
around the world have adopted approaches to
data gathering, dissemination, and utilization
that have come to be called “community indicator
projects” (Besleme & Mullin, 1997; Dluhy &
Swartz, 2006). In general the goal of these efforts
is to improve the quality of life, though the connection between the assembled data and the
resulting actions remains to be fully realized
and evaluated (Zachary, Brutschy, West,
Keenan, & Stevens, 2010). One pathway worth
exploring is a model developed by Mark Friedman, Director of the Fiscal Policy Studies Institute, called “Results-Based AccountabilityTM”
(Friedman, 2005).
Dayton (population 141,527) is the largest city
in Montgomery County (Ohio’s 5th largest
county, population 535,153). The area was introduced to Results-Based AccountabilityTM when
Friedman was invited to speak to some community leaders in 1996. Members of the Family and
Children First Council (FCFC), the county’s lead
collaborative for health and human services, were
in attendance. The Council had just been formed,
and at its next meeting in March 1996, it quickly
adopted
Results-Based
AccountabilityTM
TM
(RBA ) as a guiding principle.
In Turning the Curve, published in 1998, the
FCFC introduced its use of outcomes and indicators as tools to organize its work. Outcomes were
defined “(c)ollectively . . . as a vision of the
future” which because they “. . . are of a general
nature . . . do not immediately lend themselves to
measurement” (Montgomery County Family and
Children First Council, 1998). Six outcomes were
identified: Healthy People, Young People
Succeeding, Stable Families, Positive Living
for Special Populations, Safe and Supportive
Neighborhoods, and Economic Self-Sufficiency.
Following the principles of RBATM, the FCFC
felt that it wasn’t enough just to say that they want
county residents to be healthy or the county’s
neighborhoods to be safe and supportive. They
had to describe how these conditions would be
recognized in the day-to-day life of the community. What would people see, hear, feel, observe?

Outcomes and Indicators: Dayton-Montgomery County

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Outcomes and Indicators: Dayton-Montgomery County, Table 1 The Montgomery County (OH) Family and
Children First Council has chosen six outcomes to define its work. Each outcome has an associated vision statement
Healthy People
Everyone makes choices–for themselves of for those entrusted to their care–which promote better health. Everyone gets
the information and support they need to avoid preventable health problems. Both physical and mental wellness are
valued. Everyone has access to an adequate level of healthcare, including prenatal care, from birth through death.
Young People Succeeding
Children are well prepared for learning when they start school and receive support outside of the classroom for their
efforts inside the classroom. Intellectual curiosity, skill development and achievement are valued. Young people receive
mentoring, guidance and support as they develop the capacity to differentiate between positive and negative risk
behaviors. Positive role models are plentiful, and others in the community talk to teenagers with candor and respect
about the difficult choices they face. Students finish high school ready to compete successfully in the labor market and/or
in continuing education and skills development.
Stable Families
The community respects and supports families, recognizing that family composition in a diverse society is varied.
Family members have healthy relationships with each other. Families nurture their members and provide a sense of wellbeing and safety. Family members work together and feel that they also belong to something larger than themselves.
Positive Living for Special Populations
With support from the community, special populations have the opportunity to participate in every aspect of community
living that they desire. People with significant disabilities live, learn, work and participate in typical accessible
community settings. The community respects and protects their rights and includes them as contributing members.
[Note: “Special Populations” are people of any age with significant disabilities who need assistance with basic daily
living skills to live in the most appropriate, least restrictive community setting possible and avoid inappropriate
institutionalization. This group includes people who are frail and elderly; adults with severe and persistent mental
illness; children with severe emotional disabilities; persons with alcohol and other drug dependency; persons with
mental retardation and developmental disabilities; and others who cannot perform basic life functions without
assistance.].
Safe and Supportive Neighborhoods
People live in safe, affordable housing. They have access to positive educational and cultural experiences. Recreational
centers are conveniently located and staff serve as positive role models, especially for the children. All aspects of the
environment – e.g., air, water, soil – are safe and healthy. The community values the unique attributes of each
neighborhood, whether rural or urban.
Economic Self-Sufficiency
Residents must have access to employment that provides a living wage and benefits. Barriers to employment, including
transportation and day care issues, are minimized. Adequate opportunities for lifelong learning help prepare the
workforce for the realities of 21st-century jobs. Educational, vocational training, and worker retraining services are
readily available to support the needs of residents and employers.

To answer these questions, the FCFC wrote
vision statements for each of the outcomes
describing what would be observed if each outcome were fully achieved. Table 1 lists the outcomes and their accompanying vision statements.
Because they are general statements about
desired conditions of well-being, the outcomes
themselves are not measurable. Therefore, for
each of the outcomes, the FCFC has chosen
a small number of measurable indicators which
are intended to serve as a proxy for that outcome.
Table 2 lists the indicators associated with each
outcome.

In choosing indicators, Montgomery County
gave priority to those for which local data are
available, preferably for a number of years so
that a trend line can be established. If possible,
data for the other large counties in Ohio, for the
state as a whole, and for the nation are also
available for comparisons.
The data are updated every year and published
as part of each year’s annual report. The annual
reports have been cited as an example of exemplary reporting on results (outcomes) and indicators. In addition, the data have been published for
several years on a Web site which also provides,

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Outcomes and Indicators: Dayton-Montgomery
County, Table 2 Since 1998, the indicators associated
with each outcome have been revised periodically As of
February 2013, there were 27 indicators being tracked
Healthy people
Low birthweight
Childhood obesity
Tobacco use
Access to health care
Young people succeeding
Kindergarten readiness
Student achievement – 3rd grade reading
Student achievement – 4th grade math
Ohio graduation test – 10th grade
High school graduation rate
Public school attendance
Teen pregnancy
College enrollment
College graduation rate
College persistence
Stable families
Avoiding poverty
Substantiated child abuse
Preventable child deaths
Domestic violence deaths
Positive living for special populations
Nursing home population
Employment rate for persons with a disability
Poverty rate for persons with a disability
Safe and supportive neighborhoods
Violent crime
Property crime
Voter participation
Economic self-sufficiency
Unemployment
People receiving public assistance
Median household income

when available, the indicator data for geographic
areas contained within Montgomery County. The
Web site also affords the opportunity to present
other measures in addition to the indicators associated with each outcome. Montgomery County
is one of a growing number of communities who
publish subcounty data on the internet (Howell,
Pettit, Ormond, & Kingsley, 2003; Kingsley,
2011; Stoecker, 2006).

Outcomes and Indicators: Dayton-Montgomery County

Initially, the FCFC set targets for each indicator, i.e., values to be attained within 5 years of the
release of Turning the Curve in 1998. Twelve of
the indicators being tracked in those first years
reached their targets at some point during that
time, but most of them slipped back and only
two were still “on target” when the 2003 report
was published. About half of the indicators which
slipped back still showed a net improvement
when compared to the data in the first report.
The FCFC concluded that attaining a target is
not necessarily permanent and that a more meaningful measure of long-term success might be
whether the overall trend – the “net improvement” – is in the desired direction (Montgomery
County Family and Children First Council,
2004). Each annual report includes an update on
the short-term trends (i.e., changes since the previous report) and the historical trends.
The reason for adopting an RBATM approach
is “to improve the quality of life in communities,
cities, counties, states and nations, including
everything from the well-being of children to
the creation of a sustainable environment”
(Friedman, 2005). The structure of the FCFC
and its various subcommittees “allows (it) to
align (its) energy with (its) purpose: better results
for children and families” (Montgomery County
Family and Children First Council, 2006). The
six outcomes provide the core of this Outcome
Team structure, as can be seen in Fig. 1. Five of
the outcomes each have one team, and in order to
recognize the dynamics of neighborhoods, the
outcome called “Safe and Supportive Neighborhoods” has two teams. Figure 1 also gives examples of the initiatives and projects with which
each outcome team is engaged. The Family and
Children First Council is staffed by a department
of the county government and has access, for its
initiatives and projects, to funds allocated by the
Board of County Commissioners, as well as some
state and federal funds.
As an example of “Where Results Accountability Thinking Has Worked,” Friedman (2005)
cited public school attendance in Montgomery
County, one of the indicators under the outcome
called “Young People Succeeding.” After
dropping sharply in the early 1990s, the

Outcomes and Indicators: Dayton-Montgomery County

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FAMILY AND CHILDREN FIRST COUNCIL
OUTCOME TEAM FRAMEWORK
Board of County
Commissioners
(Admin.Agent)

Family and Children
First Council

Office of Family
and Children First

Executive
Committee

Agency Directors
Committee

Service Brokers
Group
HEALTHY PEOPLE
• Low Birth Weight

• Healthcare Safety Net

• Childhood Obesity Prevention

• Access to Dental Care

• Community Health Transformation

• Tobacco-Free Living

POSITIVE LIVING FOR SPECIAL POPULATIONS
• Community Accessibility
• FASD Task Force
• Violence Against Women with Disabilities Grant

• Prevention of Chronic Diseases

YOUNG PEOPLE SUCCEEDING
• Early Care and Education
Initiative (ReadySetSoar)
• Sinclair Fast Forward Center

• Vulnerable Youth in Transition
• Montgomery County
Mentoring Collaborative

• Learn to Earn Dayton™

STABLE FAMILIES
• Supports to Improve Outcomes for Non-Custodial
Low-Income Fathers
• Prostitution Intervention Initiative

SAFE NEIGHBORHOODS
ECONOMIC SELF-SUFFICIENCY

• Targeted Youth Violence Prevention

• Ex-Offender Re-entry
Initiative

SUPPORTIVE AND ENGAGED
NEIGHBORHOODS

• Employment for
At-Risk Populations

• Providing a Community Focus on the
Creation of Transitional Jobs

• Comprehensive Neighborhood Initiative

OUTCOME TEAM DUTIES:
• Identify related strengths and weaknesses in the
community
• Research related causes and effects of related
strengths and weaknesses
• Assess needs, gaps and priorities
• Identify and research best-practice models

Outcomes and Indicators: Dayton-Montgomery
County, Fig. 1 Each outcome team is led by a pair of
community leaders serving as cochampions. The rest of

• Identify projects/subcommittee work
• Identify financial and non-financial resources
• Seek, solicit, negotiate, acquire and leverage
other resources
• Develop, recommend and implement community
strategies

the members are community volunteers with a special
interest in the team’s focus area. The framework above
reflects the structure and activities for 2012

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attendance rate rose over the next decade,
reaching a plateau for the latter half of the
2000s. Being aware of the data in the first place,
and then taking the time to disaggregate the data
by looking at subpopulations, led to some general, community-wide responses as well as to
some specific, targeted responses.
Around the world community indicator projects are in a fertile period of development and
analysis, with much attention focused on such
issues as policy, governance, and what it
means for an indicator system to achieve success
(Hezri & Dovers, 2006; Holden, 2009). In the
coming years, the ability of community indicator
projects, including the one in Montgomery
County, to connect “knowledge and policy”
(Dluhy & Swartz, 2006) – and to connect “data
to action” (Zachary et al., 2010) – will become
increasingly important.

Cross-References
▶ Community Indicator Projects
▶ Community Indicators Consortium
▶ Community QOL Measures
▶ Healthy Communities
▶ Indicators, Quality of Life
▶ Quality of Community Life Measures
▶ Quality of Life
▶ Social Indicators Movement
▶ Social Indicators Research

Outdoor Environment
Holden, M. (2009). Community interests and indicator
system success. Social Indicators Research, 92,
429–448. doi:10.1007/s11205-008-9304-x.
Howell, E. M., Pettit, K. L. S., Ormond, B. A., &
Kingsley, G. T. (2003). Using the national neighborhoods indicators partnership to improve public health.
Journal of Public Health Management Practice, 9(3),
235–242.
Kingsley, G. T. (2011). Data and comprehensive community development: Unlocking the potential. Journal of
the Institute for Comprehensive Community Development, 2(1).
Montgomery County Family and Children First Council.
(1998). Turning the curve. Dayton, OH: Montgomery
County Family and Children First Council.
Montgomery County Family and Children First Council.
(2004). 2004 progress report: Outcomes, indicators,
and strategic community initiatives. Dayton, OH:
Montgomery County Family and Children First
Council.
Montgomery County Family and Children First Council.
(2006). 2006 progress report on community outcomes,
indicators, and strategies. Dayton, OH: Montgomery
County Family and Children First Council.
Stoecker, R. (2006). Neighborhood data systems: A best
practice analysis. Community Development: Journal
of the Community Development Society, 37(4),
109–122.
Zachary, D., Brutschy, S., West, S., Keenan, T., & Stevens, A. (2010). Connecting data to action: How the
Santa Cruz County Community Assessment Project
contributes to better outcomes for youth. Applied
Research Quality Life, 5, 287–308. doi:10.1007/
s11482-010-9103-9.

Outdoor Environment
▶ Landscape and Quality of Life

References
Besleme, K., & Mullin, M. (1997). Community indicators
and healthy communities. National Civic Review,
86(1), 43–52.
Dluhy, M., & Swartz, N. (2006). Connecting knowledge
and policy: The promise of community indicators in
the United States. Social Indicators Research, 79,
1–23. doi:10.1007/s11205-005-3486-2.
Friedman, M. (2005). Trying hard is not good enough:
How to produce measurable improvements for customers and communities. Victoria, BC: Trafford
Publishing.
Hezri, A. A., & Dovers, R. S. (2006). Sustainability indicators, policy and governance: Issues for ecological
economics. Ecological Economics, 60, 86–99.
doi:10.1016/j.ecolecon.2005.11.019.

Outliers
Yan Liu
Department of ECPS, University of British
Columbia, Vancouver, BC, Canada

Synonyms
Outlying data points

Outliers

Definition
Outliers are data points that deviate markedly
from (i) the sample in which they occur or (ii)
the assumed model, be it a probability distribution or a statistical model such as the leastsquares regression model.

Description
The concern about outliers dates back to, at least,
1,777, when Bernoulli first discussed the practice
of discarding outlying observations (Beckman &
Cook, 1983). Since then, numerous studies of
outliers have been published promoting various
methods to detect outliers or deal with outliers.
A substantial review can be found in Barnett and
Lewis (1994).
A variety of definitions of outliers have been
provided in the literature, but the most widely
cited is Grubbs (1969) definition: “an outlying
observation, or outlier, is one that appears to
deviate markedly from the other members of the
sample in which it occurs” (p. 1). It is noteworthy
that Grubbs’ definition is sample-based and
a model or population, per se, is not involved in
the definition. Hence, it leaves some ambiguities,
such as “what is the criterion for this?” and “how
does one define outliers when using different
statistical models?” Building on Grubbs’ definition, Liu and Zumbo (2012) and Liu, Wu, and
Zumbo (2012) suggest a broader definition: Outliers are data points that deviate markedly from
(i) the sample in which they occur or (ii) the
assumed model, be it a probability distribution
or a statistical model such as the least-squares
regression model.
Although this definition still does not give
a criterion or operational definition, this broad
definition allows one to operationalize various
strategies for investigating outliers by defining
“markedly deviate” from “the model.” Furthermore, this broad definition is a reminder that an
outlier is defined relative to a model or assumed
distribution. For example, when studying
income, a data point may be regarded as an outlier if income is believed to be normally

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distributed, but not an outlier if income is
believed to be distributed as a skewed random
variable such as a member of the exponential,
gamma, or chi-square families of distributions.
Another commonly seen example is that an
observation may not be an outlier in terms of
a univariate distribution but becomes an outlier
when put into a multiple regression model. For
example, when regressing height onto age,
a score of six feet tall is not an outlier, but a sixfoot-tall 10-year-old may well be an outlier.
Effects of Outliers
A single outlier can seriously bias descriptive and
inferential statistics (Cohen, Cohen, West, &
Aiken, 2003; Huber, 1981). For instance, Cohen
et al. (2003, p. 392) showed that in a sample with
even one outlier, the regression coefficient
became not statistically significant and R-square
dropped from .43 in the original data (without
outliers) to zero in the presence of the outlier. In
a correlation study, Devlin, Gnanadesikan, and
Kettenring (1981) demonstrated that, using
a sample of 29 observations, the correlation coefficient can change from .99 to zero due to a single
outlier.
Outliers can result in means shifted further
either to the right or left of a distribution, variances can be inflated or deflated, regression coefficients can be biased, estimates of reliability can
be inflated, and the inferences made from t-tests
and F-tests may be biased (e.g., Lind & Zumbo,
1993; Wilcox, 2005). For example, Liu and
Zumbo (2007) and Liu, Wu, and Zumbo (2010)
showed that estimates of Cronbach’s coefficient
alpha can be severely inflated for continuous as
well as ordinal scale item responses, which can be
inflated from .40 to .90 in some cases.
Causes of Outliers
Outliers are often only discussed as errors, but
this is both limiting and problematic. Outliers can
arise from different sources. In the outlier literature, different causes have been discussed
(Barnett & Lewis, 1994; Beckman & Cook,
1983; Liu & Zumbo, 2007; Liu & Zumbo, 2012;
Liu, Zumbo, & Wu, 2012). In general, these
outlier sources can be summarized to two general

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categories. The first category usually refers to
“errors” that occur during data collection and
errors in preparing data for analysis (e.g., data
recording or entry errors). This category also
includes the unpredictable measurement-related
errors from participants, such as participants’
guessing, inattentiveness, misresponding due to
fatigue, or lack of interest in participation. The
outliers generated from such sources are illegitimate observations and should, where possible, be
corrected.
The second category occurs when researchers
unknowingly recruit some individuals who are
not members of the target population. For example, a researcher planned to target English as
second language (ESL) students and study
their language proficiency but unknowingly
includes a subgroup of individuals whose first
language is English. Another example of a subpopulation can be people who are prone to be
inattentive, and hence respondents’ misunderstanding of instructions reflects their propensities
of inattentiveness.
Outlier Simulation
Outliers arising from different sources have different characteristics. Outliers arise from errors
and are hence specific to a particular data set so
that they are a property of a sample rather than of
a population. For example, typically it does not
make sense to talk about the number of typographical errors in a population but makes sense
to talk about such errors in a sample. Outliers
arise from a subpopulation(s) when the measure
operates differently for a subpopulation/
populations, and this category of outliers correspondingly has a population characteristic with
probably different distribution (e.g., mean or
standard deviation).
Different statistical models should be used for
studying these two categories of outliers
according to their characteristics. Deterministic
and slippage models can be used for simulating
outliers arising from errors because this type of
outliers is usually sample specific and thus the
number of outliers is fixed for a sample (Barnett
& Lewis, 1994). Although both models were used
for simulating error-type outliers and shared

Outliers

some commonality, there are some differences
between them. In deterministic model, outliers
can be generated from simply multiplying or
adding/subtracting a constant to the raw scores,
which resulted in only one possible outcome, in
this case only one manipulated data set for each
outlier condition, and thus the process is
deterministic. In a slippage model, the data are
simulated based on the parameter estimates
obtained from the real data with replications for
each outlier condition, which is a typical Monte
Carlo simulation. In a slippage model, a fixed
number of aberrant observations arise independently from a modification of the original distribution; the two distributions (i.e., the original
distribution and the modified distribution) are
regarded as random variables. Therefore,
although the number of outliers was fixed for
a sample in both models, the deterministic
model deals with one possible outcome whereas
the slippage model involves a stochastic process
with random outcomes with a central tendency
across replications.
Mixture models are usually used for simulating outliers arising from a subpopulation or subpopulations. In this case of outliers, two
populations are mixed together, and the subpopulation (outliers) usually has a different probability distribution from the target population. Unlike
outliers arising from errors, these outliers are
legitimate observations and should not be treated
as errors. In addition, rejection of these outliers is
not as easy as error-type outliers because it might
be hard to identify these outliers in some situations, such as high-dimensional data. A particular
mixture model, mixture contamination model, is
usually used to mix two normal distributions
(Barnett & Lewis, 1994).
Outliers and Data Analysis
Knowing what caused outliers is very important
because outliers from different sources have different characteristics and statistical behaviors and
hence may have different impact on statistical
methods. For typographical errors and other random measurement errors, one should correct or
remove them. One can identify this type of outliers by the following widely used strategies:

Outliers

(a) the observations beyond either 2 or 3 standard
deviations (i.e., z-scores > 2/3); (b) Tukey’s
Outlier Filter (observations either less than or
larger than the criterion) (i.e., Y < Q1-1.5*InterQ
or Y > Q3 + 1.5*InterQ), which is analogous to
the boxplot; (c) median absolute deviation,
X-Med > (Med |X-Med|)*1.28/.6745 (Wilcox,
2010); (d) Cook’s distance; and (e) Mahalanobis
distance for multivariate outliers.
Many researchers assume all outliers are
errors and hence illegitimate observations that
can be easily identified using histograms or
boxplots and feel safe whenever no obvious
outlying data points are found in the data. It is
worth noting that not all outliers are errors. If
arising from subpopulations, outliers are legitimate observations and should not be treated as
errors. The standard outlier identification and
analytical tools are usually not very helpful for
this type of outliers – unintended and unknowingly included subpopulations. These subpopulations may be characterized by a complex
profile and interaction of several variables, not
just a single variable such as male/female gender, and are mixed with the target population.
The commonly recommended robust estimators
in the literature for treating outliers (e.g.,
Winsorized estimator used for replacing outlying data points and R-estimator used for
downweighting the extreme values) can be
used for this type of outliers but intend to ignore
the sources of outliers.
Outliers from subpopulations sometimes provide one with valuable information that will be
missed if one uses robust statistics. In his presidential address to the American Statistical Association, Kruskal (1988) pointed out that outliers
lead to the discovery of penicillin and will also
lead to new discoveries. A growing research has
shown that latent class analysis or latent mixture
models may be a useful tool to model
a heterogeneous population/sample when subpopulations or subgroups are unidentified or
unobserved (e.g., Lubke & Muthén, 2005;
Sawatzky, Ratner, Johnson, Kopec, & Zumbo,
2009). There is still a great need for more
research on this potential method for modeling
outliers.

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Cross-References
▶ Data Analysis
▶ Linear Regression Model
▶ Residuals, Analysis of
▶ Robust Statistical Tests

References
Barnett, V., & Lewis, T. (1994). Outliers in statistical data
(3rd ed.). New York: Wiley.
Beckman, R. J., & Cook, R. D. (1983). Outliers.
Technometrics, 25, 119–163.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003).
Applied multiple regression/correlation analysis for
the behavioral sciences (3rd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
Devlin, S. J., Gnanadesikan, R., & Kettenring, J. R.
(1981). Robust estimation of dispersion matrices and
principal components. Journal of the American Statistical Association, 76, 354–362.
Grubbs, F. E. (1969). Procedures for detecting outlying
observations in samples. Technometrics, 11, 1–21.
Huber, P. J. (1981). Robust statistics. New York: Wiley.
Kruskal, W. H. (1988). Miracles and statistics: The casual
assumption of independence (ASA presidential
address). Journal of the American Statistical Association, 83, 929–940.
Lind, J. C., & Zumbo, B. D. (1993). The continuity principle in psychological research: An introduction to
robust statistics. Canadian Psychology, 34, 407–414.
Liu, Y., Wu, A. D., & Zumbo, B. D. (2010). The impact of
outliers on Cronbach’s coefficient alpha estimate of
reliability: Ordinal/rating scale item responses. Educational and Psychological Measurement, 70, 5–21.
Liu, Y., & Zumbo, B. D. (2007). The impact of outliers on
Cronbach’s coefficient alpha estimate of reliability –
Visual analogue scales. Educational and Psychological Measurement, 67, 620–634.
Liu, Y., & Zumbo, B. D. (2012). Impact of outliers arising
from unintended and unknowingly included subpopulations on the decisions about the number of factors in
exploratory factor analysis. Educational and Psychological Measurement, 72, 388–414.
Liu, Y., Zumbo, B. D., & Wu, A. D. (2012).
A demonstration of the impact of outliers on the decisions about the number of factors in exploratory factor
analysis. Educational and Psychological Measurement, 72, 181–199.
Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21–39.
Sawatzky, R. G., Ratner, P. A., Johnson, J. L., Kopec, J., &
Zumbo, B. D. (2009). Sample heterogeneity and the
measurement structure of the multidimensional student’s life satisfaction scale. Social Indicators

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Research: International Interdisciplinary Journal for
Quality of Life Measurement, 94, 273–296.
Wilcox, R. R. (2005). Introduction to robust estimation
and hypothesis testing (2nd ed.). San Diego, CA:
Academic Press.
Wilcox, R. R. (2010). Fundamentals of modern statistical
methods: Substantially improving power and accuracy
(2nd ed.). New York: Springer.

Outlying Data Points

Oxford 12-Item Knee Score
▶ Oxford Knee Score

Outlying Data Points

Oxford 12-Item Shoulder Instability
Score

▶ Outliers

▶ Oxford Shoulder Instability Score

Overarching Theory
▶ Metatheory

Oxford 12-Item Shoulder Score
▶ Oxford Shoulder Score

Overeducated Employed People
Oxford Elbow Score
▶ Labor Markets and Underemployment
Jill Dawson
Department of Public Health, University of
Oxford, Oxford, UK

Overnutrition
▶ Malnutrition

Synonyms
OES; Oxford 12-item elbow score

Overuse Injuries
Definition
▶ Musculoskeletal Diseases

Oxford 12-Item Elbow Score
▶ Oxford Elbow Score

Oxford 12-Item Hip Score
▶ Oxford Hip Score

A 12-item patient-reported measure (questionnaire) of elbow pain and function dimensions/
domains comprises three subscales: “elbow
function” (four items), “pain” (four items), and
“social-psychological” (four items). This was
devised in 2008 (Dawson et al., 2008a, b) for
assessing outcomes of elbow surgery. Oxford
Elbow Score # Isis Innovation Limited, Oxford
UK 2008. All rights reserved.
License available via http://www.isisinnovation.com/licensing/healthoutcomes/index.
html or innovation@isis.ox.ac.uk.

Oxford Elbow Score

Description
The Oxford Elbow Score (OES) consists of
12 questionnaire items with five ordinal response
options each. Recall period is “During the past
4 weeks.” Each item response is scored 0–4, with
0 representing greater severity. Underlying the
12 items are three domains (subscales): elbow
pain, elbow function, and social-psychological
domain. Scores for each domain are calculated as
the sum of each individual item score within that
domain, which is then converted to a metric of
0–100 (lower score representing greater severity).
This was devised using in-depth interviews
with 18 patients undergoing elbow surgery,
including six patients who had already received
surgery, until saturation reached, i.e., no new
themes, based on patients’ reports in interviews,
partly with reference to other questionnaires, plus
logic, followed by repeated piloting/amending on
new sets of patients until high response rates/few
negative comments received.
Measurement properties of 17 candidate items
were assessed within a longitudinal study of 104
consecutive patients (59, 57 % male) undergoing
elbow surgery, who had pre- and 6 months postoperative assessments. Item reduction occurred
following assessments of item completion rates
and response distribution, exploratory ▶ factor
analysis to assess scale structure, then Rasch
analysis (Rasch Unidimensional Measurement
Models, 1998); (Prieto, Alonso, & Lamarca,
2003) to assess dimensionality of subscales.
Reliability
▶ Internal Consistency: ▶ Cronbach’s alpha
(Cronbach, 1951) coefficients for the three OES
domains were pain 0.89, elbow function 0.90, and
social-psychological 0.84, and the alphas following deletion of each item revealed that no one
item had a particularly large effect on the reliability of each domain.
▶ Test-Retest Reliability: This was assessed
on n ¼ 52 patients with 48 h between assessments. The intraclass correlation coefficients for
the OES scales were pain 0.98, elbow function
0.89, and social-psychological 0.87 (all statistically significant p < 0.001).

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Validity
Application of Rasch models confirmed that each
of the three subscales (or domains) identified by
the factor analysis was unidimensional with no
item significantly misfitting at the 5 % level
(highest residual ¼ 1.4). In addition, the item
thresholds were all properly ordered. Evidence
of convergent validity included correlations
(Spearman rho) between the OES domains and
scores obtained on the Mayo Elbow Performance
Score (MEPS) clinical scale (Morrey & An,
1993), the Disabilities of the Arm, Shoulder and
Hand (DASH) Outcome Measure disability/
symptom scale (Hudak, Amadio, & Bombardier,
1996), and ▶ SF-36 general health questionnaire
version II (Ware & Sherbourne, 1992);
(Jenkinson, Stewart-Brown, Petersen, & Paice,
1999). The convergent validity of the OES was
demonstrated by high correlations (i.e., r > 0.5)
between the MEPS clinical scale, the DASH, and
three OES domains. The OES elbow function
domain was strongly associated with the SF-36
physical functioning, role physical, social functioning, pain, and energy/vitality domains and
with the SF-36 PCS score. The OES pain domain
was most strongly related to the SF-36 pain and
role physical domains and to the PCS score. The
OES social-psychological domain was most
strongly related to the SF-36 role physical and
social functioning domains and to the PCS score.
Responsiveness/Sensitivity to Change: Effect
sizes for the pain and social-psychological scales
of the OES and the MEPS clinical scale were both
>1.0, indicating that both condition-specific
measures detected a very large degree of
improvement. The three OES scales produced
generally larger effect sizes (0.79, 1.14, and
1.18, respectively) than the upper-limb specific
DASH scale (0.76). Clear associations were
observed between “elbow pain” and elbow problems’ transition items and all OES and DASH
scores (all r > 0.35). The SF-36 was much less
responsive to changes following surgery.
Discussion
As a relatively short instrument designed to
assess the outcomes of elbow surgery, the OES
is a useful method to assess the outcome of elbow

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surgery from the patients’ perspective. Although
quite new, use of the OES is steadily increasing,
with translated and validated versions already
available in Danish and Dutch.

Cross-References
▶ Cronbach’s Alpha
▶ Effect Size
▶ Factor Analysis
▶ Intraclass Correlation Coefficient (ICC)
▶ Sensitivity to Change
▶ SF-36
▶ Test-Retest Reliability

Oxford Happiness Inventory

Oxford Happiness Inventory
▶ Oxford Happiness Questionnaire

Oxford Happiness Measure
▶ Oxford Happiness Questionnaire

Oxford Happiness Questionnaire
Leslie J. Francis
University of Warwick, Coventry, UK

References
Synonyms
Cronbach, L. (1951). Coefficient alpha and the internal
structure of tests. Psychometrica, 16, 297–334.
Dawson, J., Doll, H., Boller, I., Fitzpatrick, R., Little, C.,
Rees, J., et al. (2008a). Comparative responsiveness and
minimal change for the Oxford elbow score following
surgery. Quality of Life Research, 17, 1257–1267.
Dawson, J., Doll, H., Boller, I., Fitzpatrick, R., Little, C.,
Rees, J., et al. (2008b). The development and validation of a patient-reported questionnaire to assess
outcomes of elbow surgery. The Journal of Bone and
Joint Surgery. British Volume, 90-B, 466–473.
Hudak, P. L., Amadio, P. C., & Bombardier, C. (1996).
Development of an upper extremity outcome measure:
the DASH (disabilities of the arm, shoulder and hand)
[corrected]. The upper extremity collaborative group
(UECG). American Journal of Industrial Medicine,
29, 602–608. Published erratum appears in American
Journal of Industrial Medicine 30(3), 372 (1996).
Jenkinson, C., Stewart-Brown, S., Petersen, S., & Paice,
C. (1999). Evaluation of the SF-36 version II in the
United Kingdom. Journal of Epidemiology and
Community Health, 53, 46–50.
Morrey, B. F., & An, K. N. (1993). Functional evaluation of
the elbow. In B. F. Morrey (Ed.), The elbow and its
disorders (pp. 74–83). Philadelphia, PA: WB Saunders.
Prieto, L., Alonso, J., & Lamarca, R. (2003). Classical test
theory versus Rasch analysis for quality of life
questionnaire reduction. Health and Quality of Life
Outcomes, 1, 1–27.
Rasch Unidimensional Measurement Models. (1998).
RUMM2010 [Computer software]. Duncraig: RUMM
Laboratory.
Ware, J. E., & Sherbourne, C. D. (1992). The MOS
36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care, 30,
473–483.

Extraversion, stable; Oxford Happiness Inventory;
Oxford Happiness Measure

Description
Introduction
The Oxford family of psychometric instruments
designed to assess individual differences in
▶ happiness has its roots in the broader field of
social psychology shaped by Michael Argyle and
his associates. The parent instrument in the
family is the Oxford Happiness Inventory (OHI)
that subsequently gave rise to the Oxford
Happiness Questionnaire (OHQ) and the Oxford
Happiness Measure (OHM). This entry proposes
to give detailed attention to the conceptualization
and operationalization of happiness expressed
through the OHI, to explore the connection
between the OHI and a broader theory of personality, and to illustrate the contexts in which the
OHI has been employed. Against this background
attention will be drawn to the distinctive contribution made by the OHQ and the OHM.
Oxford Happiness Inventory
The Oxford Happiness Inventory was developed
by Argyle, Martin, and Crossland (1989) to

Oxford Happiness Questionnaire

reflect a well-thought through understanding of
the social-psychological nature of happiness.
They conceptualize happiness as comprising
three main psychological components which
they define as follows: the frequency and degree
of ▶ positive affect or joy, the average level of
▶ satisfaction over a period, and the absence of
negative feelings, such as ▶ anxiety and
depression.
The
Oxford
Happiness
Inventory
operationalizes this definition by designing 29
items in which each item offers the participants
the choice between four statements. Within
each set, the four statements are intended to
reflect incremental steps of happiness defined as
unhappy or mildly depressed (0), a low level of
happiness (1), a high level of happiness (2), and
mania (3). The happiness score is computed as
the sum of the values of the 29 items checked,
ranging from 0 for individuals who check all 29
unhappy or mildly depressed options to 87 for
individuals who check all 29 mania items. An
example item from the Oxford Happiness
Inventory offers the following four incremental
steps: I do not feel happy (indicative of being
unhappy or mildly depressed), I feel fairly
happy (indicative of a low level of happiness),
I am very happy (indicative of a high level of
happiness), and I am incredibly happy (indicative
of mania).
The 29 items employed in the OHI were
developed and selected in the following way.
Argyle began by reversing the 21 items of the
▶ Beck Depression Inventory (Beck, Ward,
Mendelson, Hock, & Erbaugh, 1961) and by
adding 11 further items to cover aspects of his
definition of happiness not covered by those
initial 21 items. Three of these 32 items were
subsequently dropped during the process of
scale development.
In his foundation paper, Argyle reported an
internal consistency reliability alpha coefficient
of .90 and a 7-week ▶ test-retest reliability coefficient of .78. ▶ Concurrent validity was
established against happiness ratings made by
friends at .43. ▶ Construct validity was
established against recognized measure of the
three hypothesized components of happiness:

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positive affect, satisfaction, and absence of
negative affect. For example, the OHI correlated
+.32 with the positive affect scale of the Bradburn
Balanced Affect measure (Bradburn, 1969), +.57
with Argyle’s life satisfaction index, and .52
with the Beck Depression Inventory.
OHI and Personality
One fruitful body of research has explored
the construct validity of the OHI in terms of
testing and establishing its location within the
context of the established three dimensional
model of personality proposed by Hans Eysenck
through the Eysenck Personality Questionnaire
and the Eysenck Personality Questionnaire
Revised (see Eysenck & Eysenck, 1991).
Eysenck’s model proposes that individual differences in personality can be most adequately and
economically summarized in terms of three
orthogonal higher-order factors. Eysenck’s
model also maintains a strong continuity between
individual differences in normal personality
and abnormal personality or psychological disorders. The three dimensions of Eysenck’s model
are known by the characterizing of the highscoring poles as indices of extraversion (E), neuroticism (N), and psychoticism (P). According to
Eysenck’s test manuals, high scorers on the extraversion scale are seen as being sociable individuals who like parties, have many friends, and
need to have people to talk to. They crave excitement, take chances, and are carefree, easygoing,
and optimistic. High scorers on the neuroticism
scale are seen as being anxious, worrying,
moody, and frequently depressed individuals
who are likely to sleep badly and to suffer from
various psychosomatic disorders. They find it
difficult to get back on an even keel after emotionally arousing experiences. High scorers on
the psychoticism scale are seen as tending to be
troublesome, cruel, inhumane, lacking in feeling
and empathy, and altogether insensitive. They
tend to be solitary individuals who do not fit in
well anywhere. They may be hostile to others and
aggressive. They have a liking for odd and
unusual things and a disregard for danger.
They like to make fools of other people and to
upset them.

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According to Eysenck’s theory, the two
personality dimensions of extraversion and neuroticism are fundamental to individual differences in happiness. In one of his more popular
writings, Eysenck (1983) stated his hypothesized
connection between personality and happiness in
the following way:
Happiness is a thing called stable extraversion. . .
the positive affect in happiness seemed to be
related to easy sociability, with a natural, pleasant
interaction with other people, . . . then it only
makes sense that happiness can be associated with
extraversion. Similarly, if worries and anxieties
make up negative affect in happiness, it can easily
be seen that instability and neuroticism are also
connected with happiness. (Eysenck, 1983, p. 2)

This basic thesis that happiness is a thing
called stable extraversion has now been tested
in a series of studies designed to explore the
location of the OHI within Eysenck’s dimensional model of personality among a range of
different samples. The consensus of these studies,
demonstrating positive correlations between OHI
scores and extraversion and negative correlations
between OHI scores and neuroticism, has been
interpreted as supporting the construct validity of
the OHI (see Francis, 1999; Robbins, Francis, &
Edwards 2010).
Employing the OHI
The OHI became quickly established during
the 1990s in a series of studies that began to
map the correlates of individual differences in
happiness. These studies, reviewed by Francis
(1999) and updated by Francis (2010), link
happiness positively with social competence,
self-esteem, social skills, cooperation, personal
relationships, social support, religion, engagement with serious leisure activities, intensity of
▶ music experiences, ▶ self-actualization, selfrated attractiveness, affiliative tendencies, and
creative ideation. At the same time, other studies
link happiness negatively with depression, time
spent watching television, embarrassability,
loneliness, shyness, and social anxiety.
Basic research employing the Oxford Happiness Inventory has been extended beyond the
English-speaking community by a series of

Oxford Happiness Questionnaire

studies developing forms of the instrument in
Arabic, Chinese, German, Hebrew, Japanese,
Persian, and Portuguese (see Francis, 2010).
Oxford Happiness Questionnaire
Although the Oxford Happiness Inventory
has demonstrated good psychometric properties,
there remains one significant disadvantage with
this instrument. Since each of the 29 items has
been designed with four fixed-response options,
the instrument requires quite a lengthy questionnaire. In order to address this problem, Hills and
Argyle (2002) proposed the development of
the Oxford Happiness Questionnaire, an instrument which retained the same 29 basic issues of
the parent instrument but re-expressed each issue
in terms of the conventional Likert-type response
format (with 12 of the 29 items reverse coded).
Employing the two instruments side by side in the
same study, Hills and Argyle reported
a correlation of .80 between scores recorded on
the OHI and scores recorded on the OHQ. In their
original formulation of the OHQ, Hills and
Argyle proposed a six-point scale defined as
strongly disagree, moderately disagree, slightly
disagree, slightly agree, moderately agree, and
strongly agree.
Some subsequent use of the OHQ has preferred
a five-point scale defined as agree strongly, agree,
not certain, disagree, and disagree strongly. For
example, using this modified form of the OHQ,
with a range of scores between 29 and 145, Robbins et al. (2010) reported an internal reliability
alpha coefficient of .90 and confirmed the construct validity of the instrument in terms of
a positive correlation with extraversion,
a negative correlation with neuroticism, and no
correlation with psychoticism.
As well as developing the full 29-item form of
the OHQ, Hills and Argyle also proposed a short
8-item form. The results for the full and for the
short versions were correlated .93, suggesting
that little additional advantage may be gained
by employing the longer version. The short version comprised three negatively phrased items
(I do not feel particularly pleased with the way
I am; I do not think I look attractive; I do not have

Oxford Happiness Questionnaire

particularly happy memories of the past) and five
positively phrased items (I feel that life is very
rewarding; I am well satisfied about everything in
my life; I find beauty in some things; I can fit in
everything I want to do; I feel fully mentally
alert).
Oxford Happiness Measure
A second adaptation of the Oxford Happiness
Inventory has been offered for online completion
at www.coachingtohappiness.com. This instrument was named by Elken, Francis, and Robbins
(2010) the Oxford Happiness Measure. In its
online form, the Oxford Happiness Measure has
basically taken the 29 items within the OHI
originally intended to characterize the “manic”
response (with five of these items somewhat
modified) and arranged these items for scoring
on a five-point scale from less time to more time.
The test developers of the online form have not
yet published the psychometric properties of their
instrument.
In their modification of the online instrument
for use as a paper-and-pencil test, Elken, Francis,
and Robbins proposed a five-point scale: agree
strongly, agree, not certain, disagree, and
disagree strongly, providing a range of scores
between 29 and 145. In their Estonian translation,
this instrument recorded an internal consistency
alpha reliability coefficient of .89.
Conclusion
While all three instruments (OHI, OHQ, and
OHM) clearly belong to the same family,
there remain three significant advantages with
the original form, the OHI. The carefully calibrated incremental steps of the four items
within each of the 29 themes provide a more
carefully nuanced account of individual differences in happiness. A much larger literature
exists on the application of the OHI than on
the application of the other two variants. In
this sense new work employing the OHI can
be nested within a fuller set of related data.
The translations into other languages allow
a broader range of application and contextualization using the OHI.

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When space is short and the length of the OHI
presents a problem, there remain advantages with
the OHQ in preference to the OHM. Greater
professionalism has been shown in the development of the OHQ in terms of item selection, the
inclusion of reverse-coded items, and the proper
publication of psychometric properties. When
time is short as well as space, the short 7-item
form of the OHQ provides a reasonable approximation of the larger 29-item form.

References
Argyle, M., Martin, M., & Crossland, J. (1989). Happiness
as a function of personality and social encounters. In
J. P. Forgas & J. M. Innes (Eds.), Recent advances in
social psychology: An international perspective
(pp. 189–203). Amsterdam: Elsevier Science.
Beck, T., Ward, C. H., Mendelson, M., Hock, J., &
Erbaugh, J. (1961). An inventory for measuring depression. Archives of General Psychiatry, 7, 158–216.
Bradburn, N. M. (1969). The structure of psychological
well-being. Chicago: Aldine.
Elken, A., Francis, L. J., & Robbins, M. (2010). Internal
consistency reliability of the Estonian translation of
the Oxford Happiness Measure: Contributing to
positive psychology in Estonia. In E. E. Wells (Ed.),
Psychological well-being (pp. 223–230). New York:
Nova.
Eysenck, H. J. (1983). I Do: Your guide to a happy
marriage. London: Century.
Eysenck, H. J., & Eysenck, S. B. G. (1991). Manual of the
Eysenck Personality Scales. London: Hodder and
Stoughton.
Francis, L. J. (1999). Happiness is a thing called stable
extraversion: A further examination of the relationship
between the Oxford Happiness Inventory and
Eysenck’s dimensional model of personality and
gender. Personality and Individual Differences, 26,
5–11.
Francis, L. J. (2010). Religion and happiness: Perspectives
from psychology of religion, positive psychology and
empirical theology. In J. Atherton, E. Graham, & I.
Steedman (Eds.), The practices of happiness: Political
economy, religion and wellbeing (pp. 113–124).
London: Routledge.
Hills, P., & Argyle, M. (2002). The Oxford Happiness
Questionnaire: A compact scale for the measurement
of psychological well-being. Personality and
Individual Differences, 33, 1073–1082.
Robbins, M., Francis, L. J., & Edwards, B. (2010). Happiness as stable extraversion: Internal consistency
reliability and construct validity of the Oxford Happiness Questionnaire among undergraduate students.
Current Psychology, 29, 89–94.

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Oxford Hip Score
Jill Dawson
Department of Public Health, University of
Oxford, Oxford, UK

Synonyms
OHS; Oxford 12-item hip score

Definition
A 12-item patient-reported measure (questionnaire) of hip pain and function dimensions/
domains combined in one composite scale.
Devised in 1996 (Dawson, Fitzpatrick, Carr, &
Murray, 1996a) for assessing outcomes of hip
replacement surgery. Oxford Hip Score # Isis
Innovation Limited, Oxford UK 1998. All rights
reserved. License available via http://www.isisinnovation.com/licensing/healthoutcomes/index.
html or innovation@isis.ox.ac.uk.

Oxford Hip Score

sets of patients until high response rates/few
negative comments received.
Measurement properties assessed within a longitudinal study of consecutive patients (131
women and 89 men) undergoing hip replacement,
who had pre- and 6 months postoperative
assessments.
Reliability
Internal Consistency: ▶ Cronbach’s alpha
(Cronbach, 1951) was 0.84 at the preoperative
assessment (n ¼ 219) and 0.89 at the 6-month
follow-up (n ¼ 185). Cronbach’s alpha was not
markedly improved by removal of any item from
the score.
Test-retest Reliability: Assessed on n ¼ 68
with 24 h between assessments. The estimated
mean (0.73) of score differences was not
significantly different from 0. The coefficient of
reliability was calculated as 7.27 using Bland and
Altman’s (Bland & Altman, 1986) method, and
95 % of score differences fell between 0  7.27.
Overall, 81 % of score differences lay between
0 and 4.
The intraclass correlation was subsequently
calculated as 0.90 (Delaunay et al., 2009; Naal
et al., 2009).

Description
The Oxford Hip Score (OHS) consists of 12
questionnaire items with 5 ordinal response
options each. Recall period “during the past 4
weeks.” Scoring: each item 0–4 (4 ¼ best/least
problems). All item scores summed to produce
scale 0–48 (48 ¼ best/least problems).
Scoring: each item 0–4 (4 ¼ best/least
problems). All item scores summed to produce
scale 0–48 (48 ¼ best/least problems). Note that
the recommended scoring system has changed
since the original developmental paper was
published (Murray et al., 2007).
Devised using in-depth interviews with
patients undergoing hip replacement surgery
until saturation reached, i.e., no new themes.
Based on patients’ reports in interviews, partly
with reference to other questionnaires, plus logic,
followed by repeated piloting/amending on new

Validity
Evidence of convergent validity included moderate correlations (r ¼ 0.4, p < 0.01) between the
OHS and pain and walking components of
the clinically assessed Charnley Hip score
(Charnley, 1972) before operation. There was
also moderate to high correlations (r > 0.45 to
r ¼ 0.68, p < 0.01) between the preoperative
OHS questionnaire and relevant domains of the
▶ Arthritis Impact Measurement Scales (AIMS)
(Meenan, Gertman, & Mason, 1980) and the
SF-36 (Brazier et al., 1992; Ware, Kosinski, &
Keller, 1996) (particularly physical function and
pain domains).
Responsiveness/▶ Sensitivity to Change:
Patients reported substantial improvement at the
6-month follow-up assessment. The ▶ effect size
(2.75) was larger for the OHS questionnaire than
for any of the individual subscales of the AIMS or

Oxford Instability Shoulder Score

SF-36 questionnaires. The change scores for the
OHS were significantly greater (p < 0.0001) for
patients who reported the most improvement in
their condition using transition items and for
patients undergoing primary, compared with
revision surgery (Dawson, Fitzpatrick, Murray, &
Carr, 1996b).
Discussion
As a relatively short instrument, it is particularly
appropriate for use by older people who most
often receive total hip replacement. Its measurement properties have been assessed in a number
of studies (Dawson et al., 2001; Fitzpatrick et al.,
2000; Hajat et al., 2002). The use of the OHS has
steadily increased, and it is now widely employed
in many different types of studies including
cohort studies and audits and in national joint
replacement registries. It is used worldwide with
translated and validated versions available, and in
use, in several countries.

Cross-References
▶ Arthritis Impact Measurement Scales (AIMS)
▶ Cronbach’s Alpha
▶ Effect Size
▶ Intraclass Correlation Coefficient (ICC)
▶ Sensitivity to Change
▶ SF-36

References
Bland, J. M., & Altman, D. G. (1986). Statistical methods
for assessing agreement between two methods of
clinical measurement. Lancet, 1, 307–310.
Brazier, J. E., Harper, R., Jones, N. M., O’Cathain, A.,
Thomas, K. J., Usherwood, T., et al. (1992). Validating
the SF-36 health survey questionnaire: New outcome
measure for primary care. British Medical Journal,
305, 160–164.
Charnley, J. (1972). The long-term results of low-friction
arthroplasty of the hipperformed as a primary
intervention. Journal of Bone and Joint Surgery,
54, 61–76.
Cronbach, L. (1951). Coefficient alpha and the internal
structure of tests. Psychometrica, 16, 297–334.

4553

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Dawson, J., Fitzpatrick, R., Carr, A., & Murray, D. (1996a).
Questionnaire on the perceptions of patients about total
hip replacement. Journal of Bone and Joint Surgery, 78,
185–190.
Dawson, J., Fitzpatrick, R., Murray, D., & Carr, A. (1996b).
Comparison of measures to assess outcomes in total hip
replacement surgery. Quality in Health Care, 5, 81–88.
Dawson, J., Fitzpatrick, R., Frost, S., Gundle, R.,
McLardy-Smith, P., & Murray, D. (2001). Evidence
for the validity of a patient-based instrument for
assessment of outcome after revision hip replacement.
Journal of Bone and Joint Surgery, 83-B, 1125–1129.
Delaunay, C., Epinette, J. A., Dawson, J., Murray, D., &
Jolles, B. M. (2009). Cross-cultural adaptations of the
Oxford-12 HIP score to the French speaking
population. Review Chirurgica Orthopaedics and
Traumatology, 95(2), 89–99.
Fitzpatrick, R., Morris, R., Hajat, S., Reeves, B., Murray,
D. W., Hannen, D., et al. (2000). The value of short and
simple measures to assess outcomes for patients of
total hip replacement surgery. Quality in Health
Care, 9, 146–150.
Hajat, S., Fitzpatrick, R., Morris, R., Reeves, B., Rigge, M.,
Williams, O., et al. (2002). Does waiting for total hip
replacement matter? Prospective cohort study. Journal
of Health Services Research and Policy, 7, 19–25.
Meenan, R., Gertman, P., & Mason, J. (1980). Measuring
health status in rheumatoid arthritis: The arthritis
impact measurement scales. Arthritis and Rheumatism, 23, 146–152.
Murray, D. W., Fitzpatrick, R., Rogers, K., Pandit, H.,
Beard, D. J., Carr, A. J., et al. (2007). The use of the
Oxford hip and knee scores. Journal of Bone and Joint
Surgery, 89-B, 1010–1014.
Naal, F. D., Sieverding, M., Impellizzeri, F. M., von
Knoch, F., Mannion, A. F., & Leunig, M. (2009).
Reliability and validity of the cross-culturally adapted
German Oxford hip score. Clinical Orthopaedics and
Related Research, 467(4), 952–957.
Ware, J., Jr., Kosinski, M., & Keller, S. D. (1996).
A 12-item short-form health survey: Construction of
scales and preliminary tests of reliability and validity.
Medical Care, 34, 220–233.

Oxford Instability Score
▶ Oxford Shoulder Instability Score

Oxford Instability Shoulder Score
▶ Oxford Shoulder Instability Score

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4554

Oxford Knee Score
Jill Dawson
Department of Public Health, University
of Oxford, Oxford, UK

Oxford Knee Score

questionnaires, followed by repeated piloting/
amending on new sets of patients until high
response rates/few negative comments received.
Measurement properties assessed within
a longitudinal study of 117 consecutive patients
(66 women and 51 men) undergoing knee
replacement, who had pre- and 6 months postoperative assessments.

Synonyms
OKS; Oxford 12-item knee score

Definition
A
12-item
patient-reported
measure
(questionnaire) of knee pain and function
dimensions/domains or aspects of life combined
in one composite scale. Devised in 1998 for
assessing outcomes of knee replacement surgery.
Oxford Knee Score # Isis Innovation Limited,
Oxford UK 1998. All rights reserved.
License/questionnaire
available
via
http://www.isis-innovation.com/licensing/health
outcomes/index.html or innovation@isis.ox.
ac.uk

Description
The Oxford Knee Score (OKS) consists of
12 questionnaire items with 5 ordinal response
options each. Recall period “during the last
4 weeks.” Scoring: Originally, each item was
scored 1–5 (5 ¼ worst/most severe problems),
with all item scores summed to produce scale
12–60. However, this has now changed (Murray
et al., 2007).
The recommended method of scoring is now:
each item 0–4 (4 ¼ best/least problems), with all
item scores summed to produce scale 0–48
(48 ¼ best/least problems).
The OKS was devised using in-depth interviews with patients about to undergo knee
replacement surgery until saturation reached,
i.e., no new themes emerged. Item and response
category wording based on patients’ reports in
interviews, partly with reference to other

Reliability
Internal Consistency: ▶ Cronbach’s alpha
(Cronbach, 1951) was 0.87 before surgery
(n ¼ 117) and 0.93 at the 6-month follow-up
(n ¼ 85).
▶ Test-retest Reliability: Assessed on n ¼ 66
with 24 h between assessments. The coefficient
of reliability was 6.45 using the Bland and
Altman method (Bland & Altman, 1986); 95 %
of score differences fell between 0 and 6.45.
Overall, 89 % of score differences lay between
0 and 4 points. The intraclass correlation was
subsequently calculated as 0.94 for the preoperative data (Dawson, Fitzpatrick, Churchman,
Verjee-Lorenz, & Clayson, 2010).

Validity
Evidence of ▶ convergent validity included moderate correlations between the OKS and both
components of the clinically assessed American
Knee Society score before operation. There was
also significant correlation (r > 0.5 to r ¼ 0.71,
p < 0.01) between the OKS questionnaire and
relevant domains of the ▶ SF-36 (physical
function, role physical, pain, and social function)
and with both components of the Health Assessment Questionnaire (pain VAS and the disability
index).
Responsiveness/▶ Sensitivity to Change:
Patients reported substantial improvement at the
6-month follow-up assessment. The ▶ effect size
(2.19) was larger for the OKS questionnaire than
for any of the individual subscales of the SF-36
questionnaire. The change scores for the OKS
were significantly greater (p < 0.0001) for
patients who reported the most improvement in
their condition using transition items.

Oxford Shoulder Instability Score

Discussion
As a relatively short instrument, it is particularly
appropriate for use by older people who most
often receive total knee replacement. Its measurement properties have been assessed in a number
of studies (Davies, 2002; Dunbar, Robertsson,
Ryd, & Lidgren, 2000; Naal et al., 2009) including one demonstrating that the OKS scale meets
the expectations of the Rasch measurement
model. This confirmed it as a unidimensional
scale that appears to measure both pain and
function as a higher order construct combining
both attributes (Conaghan, Emerton, & Tennant,
2007). The use of the OKS has steadily increased,
and it is now widely employed in many different
types of studies including cohort studies and
audits and in national joint replacement
registries. It is used worldwide with translated
and validated versions available, and in use, in
several countries.
In a systematic review of patient-assessed
health instruments for the knee (Garratt, Brealey,
Gillespie, & DAMASK Trial Team, 2004), of the
16 instruments identified and assessed, the OKS
was one of only two instruments to demonstrate
good evidence for reliability, content validity,
construct validity, and sensitivity to change.

Cross-References
▶ Convergent Validity
▶ Cronbach’s Alpha
▶ Effect Size
▶ Intraclass Correlation Coefficient (ICC)
▶ Sensitivity to Change
▶ SF-36
▶ Test-Retest Reliability

4555

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Cronbach, L. (1951). Coefficient alpha and the internal
structure of tests. Psychometrica, 16, 297–334.
Davies, A. P. (2002). Rating systems for total knee
replacement. The Knee, 9, 261–266.
Dawson, J., Fitzpatrick, R., Churchman, D., VerjeeLorenz, A., & Clayson, D. (2010). User manual for
the Oxford knee score (OKS). Oxford, UK: Isis
Outcomes, Isis Innovation.
Dawson, J., Fitzpatrick, R., Murray, D., & Carr, A. (1998).
Questionnaire on the perceptions of patients about
total knee replacement. The Journal of Bone and
Joint Surgery. British Volume, 80, 63–69.
Dunbar, M. J., Robertsson, O., Ryd, L., & Lidgren, L.
(2000). Translation and validation of the Oxford-12
item knee score for use in Sweden. Acta Orthopaedica
Scandinavica, 71, 268–274.
Garratt, A. M., Brealey, S., Gillespie, W. J., & DAMASK
Trial Team. (2004). Patient-assessed health instruments for the knee: A structured review.
Rheumatology, 43, 1414–1423.
Murray, D. W., Fitzpatrick, R., Rogers, K., Pandit, H.,
Beard, D. J., Carr, A. J., et al. (2007). The use of the
Oxford hip and knee scores. The Journal of Bone and
Joint Surgery. British Volume, 89-B, 1010–1014.
Naal, F. D., Impellizzeri, F. M., Sieverding, M., Loibl, M.,
von Knoch, F., Mannion, A. F., et al. (2009). The 12item Oxford knee score: Cross-cultural adaptation into
German and assessment of its psychometric properties
in patients with osteoarthritis of the knee. Osteoarthritis and Cartilage, 17, 49–52.

Oxford Shoulder Instability
Questionnaire
▶ Oxford Shoulder Instability Score

Oxford Shoulder Instability Score
Jill Dawson
Department of Public Health, University of
Oxford, Oxford, UK

References
Bland, J. M., & Altman, D. G. (1986). Statistical methods
for assessing agreement between two methods of
clinical measurement. Lancet, 1, 307–310.
Conaghan, P. G., Emerton, M., & Tennant, A. (2007).
Internal construct validity of the Oxford knee scale:
Evidence from Rasch measurement. Arthritis Care
and Research, 57(8), 1363–1367.

Synonyms
OISS; OSIQ; OSIS; Oxford 12-item shoulder
instability score; Oxford instability score; Oxford
instability shoulder score; Oxford shoulder instability questionnaire

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Oxford Shoulder Instability Score

Definition

92 consecutive patients (53 men and 35 women;
median age of 25 years), about to receive either
a course of physiotherapy or shoulder surgery to
treat/correct their shoulder instability problem,
who completed pre-intervention and 6-month
post-intervention assessments.

A 12-item patient-reported measure (questionnaire) of shoulder dislocation/subluxation events,
pain,
and
function
dimensions/domains
combined in one composite scale. (Devised in
1999 (Dawson, Fitzpatrick, & Carr, 1999) and
for assessing outcomes of shoulder surgery for
conditions associated with shoulder instability,
including recurrent dislocation/subluxation.
Oxford Shoulder Instability Score # Isis Innovation Limited, Oxford University, Oxford UK
1999. All rights reserved.)
Licence available via http://www.isis-innovation.com/licensing/healthoutcomes/index.html
or innovation@isis.ox.ac.uk

Description
The Oxford shoulder instability score (OSIS)
consists of 12 questionnaire items with 5 ordinal
response options each. Recall period “during
the last 6 months” for 1 item; “during the last
3 months” for six items, and “during the last
4 weeks” for five items. Scoring of each item is
0–4 (4 ¼ best/least problems). All item scores
summed to produce a single composite scale of
0–48 (48 ¼ best/least problems). Note that, as
with the Oxford shoulder score (OSS), the
recommended scoring system has changed
since the original developmental paper was
published (Dawson et al., 1999; Dawson, Rogers,
Fitzpatrick, & Carr, 2009). Also, because one of
the items has a recall period of 6 months,
6 months is the earliest point/date following an
intervention at which the OSIS may be used for
any follow-up to assess outcomes. Devised using
in-depth interviews with 20 patients attending
surgical out-patient clinics for treatment of
shoulder instability, until saturation reached,
i.e., no new themes, based on patients’ reports
in interviews, partly with reference to other questionnaires, items developed, then repeatedly
piloted/reduced/amended with new sets of
patients until high response rates/few negative
comments received. Measurement properties
assessed within a longitudinal study of

Reliability
Internal consistency: ▶ Cronbach’s alpha
(Cronbach, 1951) was 0.91 at the preintervention assessment (n ¼ 92) and 0.92 at
the 6-month follow-up (n ¼ 64). All items correlated with the total score of >0.5. ▶ Test-retest
reliability is assessed in the developmental study
(Dawson et al., 1999) on n ¼ 43 with 24 h
between assessments. The estimated mean
(0.26) of score differences was not significantly
different from 0. The coefficient of reliability was
calculated as 5.7 using the Bland and Altman
(1986) method, and 95 % of score differences
fell between 0 and 5.7. Overall, 88 % of score
differences lay between 0 and 4 points. The
▶ intraclass correlation coefficient (ICC)
has since been calculated as 0.90 (95 % C.I.
0.74, 0.96) (Moser et al., 2008).
Validity
Evidence of convergent validity included large
correlations between the OSIS and the clinically
assessed Rowe standard rating scale (Rowe,
Patel, & Southmayd, 1978) (pre-op, r ¼ 0.51,
p < 0.01; post-op, r ¼ 0.84, p < 0.01) and the
Constant shoulder score (Constant & Murley,
1987) (pre-op, r ¼ 0.56, p < 0.01; post-op,
r ¼ 0.76, p < 0.01). There were also moderate
to high correlations (r > 0.69 to r ¼ 0.71,
p < 0.01) between the preoperative OSIS questionnaire and relevant domains of the ▶ SF-36
Health Survey (Brazier et al., 1992) (particularly
physical function, role physical, and pain
domains).
Responsiveness/Sensitivity to Change
Patients reported substantial improvement at
the 6-month follow-up assessment. The ▶ effect
size (0.8) was large for the OSIS questionnaire,
although the effect size for the clinically
assessed, shoulder instability-specific Rowe was

Oxford Shoulder Instability Score

even larger at 1.2. The constant clinical
score attained only a small effect size: 0.2. The
OSIS effect size was much larger than any
attained by individual subscales of the SF-36
questionnaire, where the largest effect size was
0.5 (for the role physical domain). The change
scores for the OSIS were significantly greater
(p < 0.01) for patients who rated their treatment
as “very successful” compared with all other
patient responses to that item. Change scores on
all other measures (either patient or clinician
assessed) did not detect this difference.
The change scores for the OSIS were also significantly greater (p < 0.001) for patients
who reported the most improvement in their
shoulder problem using a ▶ Transition questions
or items compared with lesser ratings. Here, neither clinical assessment distinguished these differences, while the SF-36 health perceptions and
role physical domains did produce significant
differences (p < 0.05) in their change scores
when comparing these groups.
Additional measurement details regarding
interpretability (e.g., ▶ standard error of
measurement and minimal clinically important
difference) have also been reported (Moser
et al., 2008).

4557

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instruction, for at least 4 weeks. This, together
with apprehension, means that individuals may
not attempt to test a full range of arm movements
and activities that might reasonably provoke
a recurrence of the problem (Robinson, Howes,
Murdoch, Will, & Graham, 2006) until quite
a long period following treatment. Use of the
OSIS is gradually increasing and it has now
been employed in many studies, including cohort
studies and randomized controlled trials (RCTs).
It has also been used in a number of countries.

Cross-References
▶ Convergent Validity
▶ Cronbach’s Alpha
▶ Effect Size
▶ Intraclass Correlation Coefficient (ICC)
▶ Minimal Clinically Important Difference
(MCID)
▶ Sensitivity to Change
▶ SF-36 Health Survey
▶ Standard Error of Measurement
▶ Test-Retest Reliability
▶ Transition Questions or Items

O
Discussion
The assessment of health status/health-related
quality of life and outcomes in people
with shoulder instability problems is problematic, as symptoms are often intermittent so that
at other times the problem is perceived as being
mainly about apprehension in relation to particular activities, due to the anticipation of problems,
rather than about the constant presence of
symptoms, such as pain. This means that assessments such as patient-reported outcome measures
(PROMs) used to assess a range of shoulder
problems may be inadequate (and inappropriate)
for assessing the particular problem of shoulder
instability. The use of the OSIS addresses apprehension as well as overt symptoms. The rather
long recall period(s) addressed by some items
(6 months, 3 months) reflects the fact that treatment may frequently include restricting shoulder
movement, through the use of a sling and/or

References
Bland, J. M., & Altman, D. G. (1986). Statistical methods
for assessing agreement between two methods of clinical measurement. The Lancet, 1, 307–310.
Brazier, J. E., Harper, R., Jones, N. M., O’Cathain, A.,
Thomas, K. J., Usherwood, T., et al. (1992). Validating
the SF-36 health survey questionnaire: New outcome
measure for primary care. BMJ, 305, 160–164.
Constant, C. R., & Murley, A. H. (1987). A clinical
method of functional assessment of the shoulder. Clinical Orthopaedics and Related Research, 214,
160–164.
Cronbach, L. (1951). Coefficient alpha and the internal
structure of tests. Psychometrika, 16, 297–334.
Dawson, J., Fitzpatrick, R., & Carr, A. (1999). The assessment of shoulder instability: The development and
validation of a questionnaire. The Journal of Bone
and Joint Surgery, 81-B, 420–426.
Dawson, J., Rogers, K., Fitzpatrick, R., & Carr, A. (2009).
The Oxford shoulder score revisited. Archives of
Orthopaedic and Trauma Surgery, 129, 119–123.
Epub January 9, 2008.

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4558

Moser, J. S., Barker, K. L., Doll, H. A., & Carr, A. J.
(2008). Comparison of two patient-based outcome
measures for shoulder instability after nonoperative
treatment. Journal of Shoulder and Elbow Surgery,
17(6), 886–892.
Robinson, C. M., Howes, J., Murdoch, H., Will, E., &
Graham, C. (2006). Functional outcome and risk of
recurrent instability after primary traumatic anterior
shoulder dislocation in young patients. The Journal
of Bone and Joint Surgery, 88, 2326–2336.
Rowe, C., Patel, D., & Southmayd, W. (1978). The
Bankart procedure. The Journal of Bone and Joint
Surgery. American Volume, 60-A, 1–15.

Oxford Shoulder Score
Jill Dawson
Department of Public Health, University
of Oxford, Oxford, UK

Oxford Shoulder Score

options each. Recall period “during the last
4 weeks” (Dawson et al., 1996).
Scoring: each item 0–4 (4 ¼ best/least problems). All item scores summed to produce scale
0–48 (48 ¼ best/least problems). Note that the
recommended scoring system has changed since
the original developmental paper was published
(Dawson, Rogers, Fitzpatrick, & Carr, 2009).
Devised using in-depth interviews with 20
patients undergoing shoulder surgery until
saturation reached, i.e., no new themes, based
on patients’ reports in interviews, partly with
reference to other questionnaires, plus logic,
followed by repeated piloting/amending on new
sets of patients until high response rates/few
negative comments received.
Measurement properties assessed within
a longitudinal study of 111 consecutive patients
(51 women and 60 men) undergoing shoulder
surgery, who had pre- and 6 months postoperative
assessments.

Synonyms
OSS; Oxford 12-item shoulder score

Definition
A 12-item patient-reported measure (questionnaire) of shoulder pain and function dimensions/
domains combined in one composite scale.
Devised in 1996 (Dawson, Fitzpatrick, & Carr,
1996) and for assessing outcomes of shoulder
surgery for degenerative conditions (excluding
conditions of shoulder instability, for which the
Oxford Shoulder Instability Score (Dawson,
Fitzpatrick, & Carr, 1999) was developed).
Oxford Shoulder Score # Isis Innovation
Limited, Oxford UK 1998. All rights reserved.
License
available
via
http://www.
isis-innovation.com/licensing/healthoutcomes/
index.html or innovation@isis.ox.ac.uk

Description
The Oxford Shoulder Score (OSS) consists of
12 questionnaire items with 5 ordinal response

Reliability
Internal Consistency: Cronbach’s alpha
(Cronbach, 1951) was 0.89 at the preoperative
assessment (n ¼ 111) and 0.92 at the 6-month
follow-up (n ¼ 56). All items correlated with the
total score at >0.4. Test-Retest Reliability:
assessed on n ¼ 68 with 24 h between assessments. The estimated mean (0.12) of score
differences was not significantly different from
0. The coefficient of reliability was calculated as
6.8 using the Bland and Altman (Bland &
Altman, 1986) method and 95 % of score differences fell between 0 and 6.8. Overall, 83 % of
score differences lay between 0 and 4 points.
The intraclass correlation was subsequently calculated as 0.83 (Ekeberg et al., 2008).
Validity
Evidence of convergent validity included large
correlations (pre-op: r ¼ 0.74 p < 0.01, post-op:
r ¼ 0.75 p < 0.01) between the OSS and the
clinically assessed Constant Shoulder score
(Constant & Murley, 1987). There was
also moderate to high correlations (r > 0.49 to
r ¼ 0.86, p < 0.01) between the preoperative
OSS questionnaire and relevant domains of the

Ozone

Health Assessment Questionnaire (HAQ) (Fries,
Spitz, & Young, 1982) and the SF-36 (Brazier
et al., 1992) (particularly physical function and
pain domains).
Responsiveness/Sensitivity
to
Change:
Patients reported substantial improvement at the
6-month follow-up assessment. The effect size
(1.2) was slightly larger for the OSS questionnaire than for any of the individual subscales of
the HAQ or SF-36 questionnaires. The change
scores for the OSS were significantly greater
(p < 0.001) for patients who reported the most
improvement in their condition using transition
items.

Discussion
As a relatively short instrument, it is particularly
appropriate for use by older people who
frequently receive shoulder surgery (including
shoulder replacement). Its measurement properties have been assessed in a number of studies
(Kirkley, Griffin, & Dainty, 2003). The use of the
OSS has steadily increased, and it is now
employed in many different types of studies
including cohort studies and randomized
controlled trials (RCTs) and in national joint
replacement registries. It is used worldwide with
translated and validated versions available, and in
use, in a number of countries.

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Brazier, J. E., Harper, R., Jones, N. M., O’Cathain, A.,
Thomas, K. J., Usherwood, T., et al. (1992). Validating
the SF-36 health survey questionnaire: New outcome
measure for primary care. BMJ, 305, 160–164.
Constant, C. R., & Murley, A. H. (1987). A clinical
method of functional assessment of the shoulder.
Clinical Orthopaedics and Related Research, 214,
160–164.
Cronbach, L. (1951). Coefficient alpha and the internal
structure of tests. Psychometrica, 16, 297–334.
Dawson, J., Fitzpatrick, R., & Carr, A. (1996). Questionnaire on the perceptions of patients about shoulder
surgery. The Journal of Bone and Joint Surgery.
British Volume, 78, 593–600.
Dawson, J., Fitzpatrick, R., & Carr, A. (1999). The assessment of shoulder instability: The development and
validation of a questionnaire. The Journal of Bone
and Joint Surgery. British Volume, 81-B, 420–426.
Dawson, J., Rogers, K., Fitzpatrick, R., & Carr, A. (2009).
The Oxford shoulder score revisited. Archives of
Orthopaedic and Trauma Surgery, 129, 119–123.
Ekeberg, O. M., Bautz-Holter, E., Tveita, E. K., Keller, A.,
Juel, N. G., & Brox, J. I. (2008). Agreement, reliability
and validity in 3 shoulder questionnaires in patients
with rotator cuff disease. BMC Musculoskeletal Disorders, 9, 68.
Fries, J. F., Spitz, P. W., & Young, D. Y. (1982). The
dimensions of health outcomes: The health assessment
questionnaire, disability and pain scales. Journal of
Rheumatology, 9, 789–793.
Kirkley, A., Griffin, S., & Dainty, K. (2003). Scoring
systems for the functional assessment of the shoulder.
Arthroscopy, 19, 1109–1120.

O
Oz
▶ Australia, Quality of Life

Cross-References
▶ Cronbach’s Alpha
▶ Effect Size
▶ Intraclass Correlation Coefficient (ICC)
▶ Sensitivity to Change
▶ SF-36
▶ Test-Retest Reliability

References
Bland, J. M., & Altman, D. G. (1986). Statistical methods
for assessing agreement between two methods of
clinical measurement. Lancet, 1, 307–310.

Ozone
Tor H. Oiamo
Department of Geography, Western University
Faculty of Social Science, London, ON, Canada

Synonyms
Ground-level
ozone;
Ozone,
surface;
Stratospheric ozone; Trioxygen (O3); Tropospheric ozone

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Ozone

Definition

also influenced by environmental factors such as
climate change and solar flares.
Levels of ground-level ozone, or bad ozone,
are highly variable and depend on the proximity
and concentration of local anthropogenic
sources. The National Air Pollution Surveillance
Network in Canada has recorded concentrations
ranging from below 5 to above 200 ppb in Vancouver. The US Environmental Protection
Agency (EPA) estimates that motor vehicles contribute about 50 % of the ozone precursors, while
other major sources include industrial and commercial processing, utilities, residential fuel combustion, and consumer solvents. Background
concentrations are defined as the fraction of
ozone in a given area that is not attributed to
anthropogenic sources of local origin, and though
the rate of increase is currently lower than during
the 1970s and 1980s, these levels continue to rise.
In 2004, annual median levels of background
ozone around the world ranged from 23 to 34
parts per billion (ppb) (Vingarzan, 2004). Projections of future levels depend on emission scenarios, but current trends suggest that background
levels of surface ozone may rise to levels that
exceed accepted criteria for human health and
the environment. The additional contribution of
local sources to background levels means that
areas with human activities have higher levels
of ground-level ozone.
Due to ▶ health impacts and common occurrence of ozone, many countries have adopted
▶ air quality standards that set guidelines for
exposure. The US EPA 8-h average national standard is 75 ppb, above which higher levels put
different populations at risk. Ground-level
ozone at levels exceeding the national standard
may be unhealthy for sensitive groups; levels
above 96 ppb are considered unhealthy, levels
above 116 ppb very unhealthy, and above
405 ppb hazardous. Air quality indices in other
countries are by and large similar and focus on
the impacts on vulnerable groups such as those
with preexisting cardiovascular and respiratory
conditions at lower concentrations, though the
guides differ in their reference to healthy
populations. Ozone is usually included along
with sulfur oxides, nitrogen oxides, and

Gaseous ozone is a reactive triatomic allotrope of
oxygen with half-life ranging from approximately 3 months at 50  C to 3 days at 20  C
before it breaks down to dioxygen. Ozone is
therefore more stable in the stratospheric ozone
layer, where it is naturally formed by photochemical reactions between ultraviolet (UV) radiation
from the Sun and oxygen, a process that also
protects life on Earth from the damaging effects
of UV radiation. While 90 % of atmospheric
ozone can be found in the ozone layer, groundlevel or tropospheric ozone is primarily formed
by photochemical reactions with the precursors
nitrogen oxides (NOx) and volatile organic compounds (VOCs) from anthropogenic sources.
Natural sources of ground-level ozone include
biomass burning and lightning as well as the
stratosphere.

Description
Tropospheric and stratospheric ozone have very
different effects on ▶ quality of life. In general,
the effects of ozone are good in the troposphere
and bad in the stratosphere and at ground level.
Although researchers were already studying the
health effects of ozone in the 1960s, the topic
gained significant interest during the 1980s with
the growing awareness of hydrofluorocarbons
(HFCs), chlorine and bromine gases, and their
ability to destroy the ozone layer (Lippman,
1989; Weatherhead & Andersen, 2006). The discovery of ozone loss over Antarctica in 1985 (see
Solomon, Garcia, Rowland, & Wuebbles, 1986)
led to an international effort to halt the production
and use of ozone-depleting substances, which
culminated in the ratification of the Montreal
Protocol in 1989. This led to a significant reduction in stratospheric chlorine loading to levels
observed before 1980. Globally averaged depletion of stratospheric ozone peaked at approximately 5 % in 1990 and is currently at about
3.5 % of the 1964–1980 average (Fahey &
Hegglin, 2011). It is not known when or if the
ozone layer will return to previous levels as it is

Ozone, Surface

particulate matter in composite air quality indices
used to advise the public on air quality and is an
important contributor to photochemical smog.
As suggested by the various air quality indices, the effects of ground-level ozone on quality
of life are most significant in people with impairments of the respiratory, cardiovascular, and
immune systems (Dockery & Pope, 1994; Jerret
et al., 2009). A study of ▶ mortality in 95 US
cities over a 14-year period found that a 10-ppb
increase in the previous week’s ozone was associated with 0.5 % increase in daily mortality and
0.6 % increase in cardiovascular and respiratory
mortality (Bell, McDermott, Zeger, Samet, &
Dominici, 2004). However, decreased lung function in healthy subjects exposed to transient doses
of ozone is also well established (Lippman,
1989), and a study of children found that ozone
exacerbated the development of ▶ asthma among
those who exercised (McConnell et al., 2002).
Quality of life impacts of stratospheric ozone
depletion are much more ubiquitous and due to
increased solar UV-B radiation at the surface of
the Earth. Although UV-B radiation is crucial for
vitamin D production, harmful effects include
eye and skin diseases such as cancer (Norval
et al., 2011). Awareness of these risks has led to
▶ public health initiatives aimed at decreasing
the duration and intensity of Sun exposure.

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Dockery, D. W., & Pope, C. A., III. (1994). Acute respiratory effects of particulate air pollution. Annual
Review of Public Health, 15, 107–132.
Fahey, D. W., & Hegglin, M. I. (Coordinating Lead
Authors). (2011). Twenty questions and answers
about the ozone layer: 2010 update, scientific assessment of ozone depletion: 2010 (p. 72). Geneva, Switzerland: World Meteorological Organization.
(Reprinted from Scientific Assessment of Ozone Depletion: 2010 (Global Ozone Research and Monitoring
Project- Report No. 52, p. 516). Geneva, Switzerland:
World Meteorological Organization, 2011).
Jerret, M., Burnett, R. T., Pope, C. A., III, Ito, K.,
Thurston, G., Krewski, D., et al. (2009). Long-term
ozone exposure and mortality. The New England Journal of Medicine, 360, 1085–1095.
Lippman, M. (1989). Health effects of ozone – A critical
review. Journal of the Air & Waste Management
Association, 39, 672–695.
McConnell, R., Berhane, K., Gilliland, F., London, S. J.,
Islam, T., Gauderman, W. J., et al. (2002). Asthma in
exercising children exposed to ozone: A cohort study.
The Lancet, 359, 386–391.
Norval, M., Lucas, R. M., Cullen, A. P., de Guijl, F. R.,
Longstreth, J., Takizawa, Y., et al. (2011). The human
health effects of ozone depletion and interactions with
climate change. Photochemical and Photobiological
Sciences, 10, 199–225.
Solomon, S., Garcia, R. R., Rowland, F. S., & Wuebbles,
D. J. (1986). On the depletion of Antarctic ozone.
Nature, 321, 755–758.
Vingarzan, R. (2004). A review of surface ozone background levels and trends. Atmospheric Environment,
34, 3431–3442.
Weatherhead, E. C., & Andersen, S. B. (2006). The search
for signs of recovery of the ozone layer. Nature, 441,
39–45.

References
Bell, M. L., McDermott, A., Zeger, S. L., Samet, J. M., &
Dominici, F. (2004). Ozone and short-term mortality
in 95 US urban communities, 1987–2000. Journal of
the American Medical Association, 292, 2372–2378.

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Ozone, Surface
▶ Ozone

O