Psychological Assessment
2007, Vol. 19, No. 3, 318 –329

Copyright 2007 by the American Psychological Association
1040-3590/07/$12.00 DOI: 10.1037/1040-3590.19.3.318

The Validity and Reliability of the Violence Risk Scale—Sexual Offender
Version: Assessing Sex Offender Risk and Evaluating Therapeutic Change
Mark E. Olver

Stephen C. P. Wong

University of Saskatchewan

Regional Psychiatric Centre of the Correctional Service of
Canada and the University of Saskatchewan

Terry Nicholaichuk and Audrey Gordon
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Regional Psychiatric Centre of the Correctional Service of Canada
The Violence Risk Scale—Sexual Offender version (VRS–SO) is a rating scale designed to assess risk
and predict sexual recidivism, to measure and link treatment changes to sexual recidivism, and to inform
the delivery of sexual offender treatment. The VRS–SO comprises 7 static and 17 dynamic items
empirically or conceptually linked to sexual recidivism. Dynamic items with higher ratings identify
treatment targets linked to sexual offending. A modified stages of change model assesses the offender’s
treatment readiness and change. File-based VRS–SO ratings were completed on 321 sex offenders
followed up an average of 10 years postrelease. VRS–SO scores predicted sexual and nonsexual violent
recidivism postrelease and demonstrated acceptable interrater reliability and concurrent validity. A factor
analysis of the dynamic items generated 3 factors labeled Sexual Deviance, Criminality, and Treatment
Responsivity, all of which predicted sexual recidivism and were differentially associated with different
sex offender types. The dynamic items together made incremental contributions to sexual recidivism
prediction after static risk was controlled for. Positive changes in the dynamic items, measured at pre- and
posttreatment, were significantly related to reductions in sexual recidivism after risk and follow-up time
were controlled for, suggesting that dynamic items are indeed dynamic or changeable in nature.
Keywords: Violence Risk Scale—Sexual Offender version (VRS–SO), static and dynamic variables,
sexual recidivism, prediction, change

The sexual abuse of adults and children is an egregious societal
concern. Despite gross underreporting (Lisak & Miller, 2002), as
many as 40% of Canadian women report having been sexually
assaulted since age 16 (Johnson & Sacco, 1995). Sexual abuse also
has profound long-term sequelae including emotional, relationship, and mental health concerns (Browne & Finkelhor, 1986). As
such, the assessment, treatment, and management of individuals at
high risk for sexual recidivism are paramount in the reduction of
sexual violence.

Static and Dynamic Predictors of Sexual Recidivism and
Current Risk Instruments
Although predicting sexual violence is important, reducing and
preventing such violence should be the ultimate goal of risk assessment (Douglas & Kropp, 2002; Wong & Gordon, 2006). Recent
meta-analytic findings have confirmed that sex offender treatment
using cognitive-behavioral relapse prevention approaches has the
potential to reduce sexual recidivism (Hanson et al., 2002). It follows
that sexual recidivism risk is arguably dynamic and can potentially
change through intervention or experience.
Large-scale meta-analytic reviews (see Hanson & Bussière,
1998; Hanson & Morton-Bourgon, 2004) have identified static risk
predictors (e.g., offense history, offender and victim demographics) that have been combined to develop sexual offender recidivism prediction tools such as the Static 99 (Hanson & Thornton,
1999), the Sex Offender Risk Appraisal Guide (Quinsey, Rice, &
Harris, 1995), and the Minnesota Sex Offender Screening Tool—
Revised (Epperson, Kaul, & Hesselton, 1998). Although these
static tools can predict sexual recidivism quite well, given their
static nature they do little to inform sex offender treatment, which
requires assessing dynamic, or changeable, risk variables to identify treatment targets and measure change.
Dynamic risk predictors or variables, also known as criminogenic needs (Andrews & Bonta, 2003) or variable risk factors
(Kraemer et al., 1997), have been defined as “changeable or
potentially changeable variables that could be influenced or

Mark E. Olver, Department of Psychology, University of Saskatchewan,
Saskatoon, Saskatchewan, Canada; Stephen C. P. Wong, Regional Psychiatric Centre of the Correctional Service of Canada, Saskatoon, Saskatchewan, Canada, and the Department of Psychology, University of Saskatchewan; Terry Nicholaichuk and Audrey Gordon, Regional Psychiatric Centre
of the Correctional Service of Canada.
This study was supported by Social Sciences and Humanities Research
Council Grant 752-2001-2164 awarded to Mark E. Olver and was based on
a doctoral dissertation. We thank the Correctional Service of Canada for
providing access to data and resources for the current study and Brenda
Maire and Linda Flahr for their assistance in data collection. The views
expressed in this article do not necessarily represent the views of the
Correctional Service of Canada.
Correspondence concerning this article should be addressed to Mark E.
Olver, Department of Psychology, University of Saskatchewan, Arts Building Room 154, 9 Campus Drive, Saskatoon, Saskatchewan, Canada S7N
5A5. E-mail: mark.olver@usask.ca
318

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

SEX OFFENDER RISK ASSESSMENT

changed by psychological, social or physiological means such as
treatment interventions” (Wong & Gordon, 2006, p. 283). Changes
occurring in dynamic risk variables should be linked to changes in
recidivism. Although both static and dynamic variables can assess
sexual recidivism risk, dynamic variables can ostensibly inform
treatment planning, identify treatment targets, and measure
changes in risk (Beech, Friendship, Erikson, & Hanson, 2002;
Hanson & Harris, 2000; Hudson, Wales, Bakker, & Ward, 2002).
Some have argued that dynamic variables are useful only if they
have incremental predictive validity and thus add to the predictive
accuracy of static variables (Harris et al., 2003). However, others
have argued, and we concur, that dynamic variables do not necessarily have to trump the predictive efficacy of static variables to
be useful (Wong & Gordon, 2006). Simply put, there is more to
dynamic variables than recidivism prediction; their utility in treatment and risk reduction is equally important. One of the challenges
in sex offender research is to identify dynamic variables likely
related to sexual recidivism as well as to validate those variables as
predictors of sexual recidivism.
Recent research has identified several putatively dynamic sexual
recidivism predictors (Beech et al., 2002; Hanson & Harris, 2000;
Hudson et al., 2002; Thornton, 2002), and meta-analytic reviews
(Hanson & Morton-Bourgon, 2004) have further identified two
broad domains with strong links to sexual recidivism—sexually
deviant interests/behaviors and an antisocial orientation/
criminality. Static and/or dynamic variables have also been combined to develop sex offender risk appraisal guides such as the Sex
Offender Need Assessment Rating (Hanson & Harris, 2001), Sexual Violence Risk–20 (Boer, Hart, Kropp, & Webster, 1997), Risk
for Sexual Violence Protocol (Hart, Kropp, & Laws, 2003) and the
Vermont Assessment of Sex Offender Risk (McGrath & Hoke,
2002). However, much of the research examining dynamic predictors of sexual recidivism has simply assumed that dynamic risk
variables are indeed dynamic while providing little evidence to
support such assumptions. Although this line of research has
shown that dynamic variables can predict sexual recidivism, the
dynamic variables in these studies have typically been assessed at
only one time point and have not been demonstrated to change.
Some exceptions include the work of Hudson et al. (2002), who
found some negative correlations between pre- and posttreatment
change scores on sex offender self-report measures (e.g., cognitive
distortions, empathy) and sexual recidivism, and the work of
Hanson (2005), who investigated the predictive validity of stable
and acute dynamic variables over multiple time points. For risk
predictors or variables to be deemed dynamic in nature, changes in
these variables (measured at two points in time) should be linked
to changes in sexual recidivism (see Douglas & Skeem, 2005;
Kraemer et al., 1997). Despite more recognition of the importance
of using dynamic variables to assess sex offender risk as well as an
increase in corresponding research, there remains a need to integrate dynamic risk variables into a sex offender risk assessment
tool to appraise risk, complement treatment, and assess treatment
change.

The Violence Risk Scale—Sexual Offender Version
(VRS–SO)
The VRS–SO (Wong, Olver, Nicholaichuk, & Gordon, 2003)
was designed to integrate sex offender risk assessment and treat-

319

ment planning, including the assessment of change, within a single
instrument. The VRS–SO uses both static and dynamic variables to
assess sexual recidivism risk and uses dynamic variables to identify treatment targets and to measure changes in risk as a result of
treatment or other change agents.
The VRS–SO assesses and measures change by using a modified application of the transtheoretical model of change (TTM;
Prochaska, DiClemente, & Norcross, 1992). The TTM posits that
individuals progress through a series of stages (precontemplation,
contemplation, preparation, action, and maintenance), characterized by different cognitive, experiential, and behavioral changes as
they attempt to remediate problem areas. Although the TTM
conceptualization of treatment change has been applied to other
forensic populations (Levesque, Gelles, & Velicer, 2000; Willoughby & Perry, 2002; Wong & Gordon, 2006), including sexual
offenders (Tierney & McCabe, 2005), it has yet to be incorporated
into a risk/need assessment tool for sexual offenders. The metric
for assessing treatment change and risk reduction on the VRS–SO
is presented in the Method section below.

Scope of the Present Study
The reliability and validity of the VRS–SO are evaluated in two
sets of analyses. First, the factor structure, descriptive statistics,
interrater reliability, internal consistency, concurrent validity, and
predictive validity of the VRS–SO are examined. Second, the
relationship between changes in the VRS–SO dynamic variables at
two time points (pre- and posttreatment) and changes in sexual
recidivism are examined to address whether changes in these
variables predict sexual recidivism and, as such, whether the
purported dynamic variables are indeed dynamic.

Method
Participants
Participants included 321 male federal offenders who had participated in a high intensity sex offender treatment program (the
Clearwater Program) in a maximum-security forensic mental
health facility in Canada between 1983 and 1997. The program is
6 – 8 months in duration, provides group and individual therapies,
and is cognitive-behavioral in orientation.1 All offenders had one
or more index convictions for sexual offenses (96.3%), or had
prior sexual charge(s) or conviction(s), or had exhibited sexually
inappropriate behaviors (3.7%). Offenders were serving a mean
sentence length of 5.8 years (SD ⫽ 3.9). The mean ages of the
sample at the time of index offense and data collection were 30.5
years (SD ⫽ 9.8) and 42.7 years (SD ⫽ 9.3), respectively. Approximately 62.6% of the offenders were Caucasian, 33.6% Aboriginal, and 2.8% other ethnic decent. Most offenders (49.3%)
were single/never married, 23.7% common law/married, 25.5%
separated/divorced, and ⬍ 1% widowed. Demographic information was incomplete for 3 (1%) of the cases. The offender sample
1

See Nicholaichuk, Gordon, Gu, and Wong (2000) for a detailed description of the Clearwater Program.

320

OLVER, WONG, NICHOLAICHUK, AND GORDON

consisted of 169 rapists, 56 child molesters, 45 mixed offenders,
and 51 incest offenders.2

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Materials
VRS–SO. The VRS–SO is modeled closely after the Violence
Risk Scale (Wong & Gordon, 1999, 2006), which is designed to
assess nonsexual violence risk and need. The VRS–SO is a 24-item
clinician-rated scale comprising 7 static and 17 dynamic items. All
items are rated on a 4-point Likert-type scale ranging from 0 to 3
on the basis of a thorough file review and a semistructured interview;3 higher ratings indicate a closer link to inappropriate sexual
or nonsexual behaviors, indicating increased sexual recidivism
risk. The static and dynamic items, including a brief description of
each item, are presented in the Appendix.
The static and dynamic components of the VRS–SO were each
developed through a different set of procedures. The static component was developed with statistical-actuarial procedures on approximately one randomly selected half of the sample (n ⫽ 152)
and cross-validated on the remaining portion of the sample (n ⫽
169). A pool of 24 static variables identified from the literature
was initially coded and correlated with sexual recidivism. Variables with the strongest univariate relationships to outcome were
retained and rescaled to a 4-point format. All 7 static items were
developed in this manner (see the Appendix) and can be summed
to arrive at a static item total.
The dynamic component was developed through a detailed
review of the sex offender prediction and treatment literature so as
to select a wide spectrum of theoretically, empirically, and
treatment-relevant dynamic risk variables to maximize the content
validity of the VRS–SO. The review included the meta-analytic
work of Hanson and Bussière (1998), research identifying dynamic
predictors (e.g., Hanson & Harris, 2000; Proulx et al., 1997), the
relapse prevention work of Pithers (1990) and Ward and Hudson
(1998), and the theoretical traditions of The Psychology of Criminal Conduct (Andrews & Bonta, 2003).
A VRS–SO scoring manual (Wong et al., 2003) that includes
detailed rating criteria for each static and dynamic item has been
developed. Each item has an “objective” section that briefly describes the underlying construct (e.g., deviant sexual preference)
together with detailed descriptions for 3-point and 0-point ratings
for the item. If the presenting problem behaviors with respect to a
given item are “less serious” than those described by a 3 rating, a
rating of 2 is given. Similarly if the presenting behaviors are “less
positive” than those described by a 0 rating, a rating of 1 is given.
Items rated a 2 or 3 are problem areas with close links to sexual
offending (i.e., are criminogenic) and should be considered as
treatment targets.
The different dynamic items are potentially changeable by varying degrees. Changes of the dynamic items are assessed and
quantified through the application of a modified TTM. Each of the
five stages of change has been operationalized for each of the 17
dynamic items. The progression in the stages of change (e.g., from
contemplation at pretreatment to action at posttreatment) demonstrates the extent to which the offender has improved by developing positive coping skills and strategies that are stable, sustainable,
and generalizable with respect to each dynamic item.
All treatment targets, that is, dynamic items rated 2 or 3, are
given a stages of change baseline rating at pretreatment to assess

the individual’s motivation and readiness for change. Dynamic
items that are not treatment targets, that is, those rated 0 or 1,
generally require no stages of change rating. The stages of change
are then rerated at posttreatment on all dynamic items identified as
treatment targets. Change is quantified by comparing the stages of
change rating for each dynamic item at pretreatment with that at
posttreatment. Advancing from one stage of change to the next on
a given item is an indication of positive change and, hence, risk
reduction.4 Progression from one stage to the next stage is scored
as a 0.5-point reduction in the pretreatment rating of the item,
progression in two stages as a 1.0-point reduction, and so on. This
is repeated for each dynamic item identified as a treatment target.5
The total point deductions for each dynamic item at posttreatment
are summed across all 17 dynamic items to arrive at a total change
score reflecting the total amount of change. The total change score
is subtracted from the total pretreatment dynamic ratings to obtain
the total posttreatment dynamic ratings.
For instance, a child molester entering sex offender treatment
may receive a 3-point rating on the item deviant sexual preference
and may be assessed as being in the contemplation stage of change
for this item. Following treatment during which the offender learns
arousal modification and fantasy monitoring strategies, major
gains are observed and progress is made to the action stage of
change. The individual has progressed two stages (i.e., contemplation to preparation to action), resulting in 2 ⫻ 0.5 ⫽ 1.0 points of
change, the change score. The change score is deducted from the
pretreatment rating of this item, resulting in a posttreatment rating
of 3 – 1 ⫽ 2. The total change score is the sum of the change scores
for all the dynamic items. The total VRS–SO pre- and posttreatment scores represent the offender’s overall risk level for sexual
recidivism at two points in time. Higher scores indicate higher risk.
As the VRS–SO was designed with an emphasis on assessing
the relationship between therapeutic change and corresponding
changes in risk, we argue that using the stages of change model to
measure therapeutic change has important advantages over measuring change by simply rerating the dynamic items at posttreatment. Some rating criteria for the VRS–SO dynamic items and in
other risk-rating tools are based on behaviors observable only in
2
Rapists were defined as perpetrators with victim(s) who were at least
14 years of age and child molesters as those with boy or girl victim(s)
under the age of 14. Mixed offenders were defined as having both child and
adult victims, and incest offenders were defined as those with victims who
were family members or relatives. Relatives are individuals sufficiently
closely related to the offender such that marriage would normally be
prohibited (Hanson & Thornton, 1999).
3
File information may be used to rate the items for research purposes.
4
An exception to this scoring rule is for progress from precontemplation
to contemplation, wherein no risk reduction is registered as there is no
relevant behavioral improvement.
5
Pretreatment: static total score ⫹ dynamic total score ⫽ VRS–SO total
pretreatment scores. Posttreatment: static total score ⫹ dynamic total score
– total change score ⫽ VRS–SO total posttreatment score. Total change
scores ⫽ sum of differences between pre- and posttreatment Stage of
Change ratings for all dynamic variables. Should the individual worsen
during treatment, a score of 0.5 is added to the individual’s item score for
every stage of deterioration. As such, it is possible for an individual’s
posttreatment score to increase (i.e., get worse) rather than decrease (i.e.,
improve).

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

SEX OFFENDER RISK ASSESSMENT

the community but not in prisons or forensic treatment settings,
which are restrictive environments. For instance, incarcerated
child molesters do not have access to potential child victims.
Similarly, it is problematic to infer sexually compulsive behaviors
when individuals do not have the freedom to engage in promiscuity, the frequent use of prostitutes, or multiple one night stands.
The stages of change descriptions of the dynamic items (the
measurements of treatment change) were developed specifically to
capture offense-linked proxy behaviors that are relevant to treatment and are observable in forensic and/or treatment settings.6
Therapists can use these observations as a metric to guide their
decisions as to how much change has occurred. Similarly, in most
forensic inpatient treatment, therapists can assess changes only by
observing in-treatment behaviors, not behaviors that can only be
observable in the community. A further advantage in using the
TTM to guide the measurement of change is that the model also
describes how offenders change in therapy in addition to assessing
how much change has occurred. Therapists can also use the stages
of change ratings to inform their conceptualization of treatment
and treatment delivery.
Static 99. The Static 99 (Hanson & Thorton, 1999) is a static
actuarial sex offender risk assessment measure composed of 10
static items pertaining to sexual and nonsexual offense history,
victim characteristics, and offender demographics. Total scores
range from 0 to 12 points and can be organized into four risk
groups: low (0 –1), medium-low (2–3), medium-high (4 –5), and
high (6 –12). The Static 99 is a widely used sexual recidivism risk
assessment tool (Hanson, 2006) and is included to assess the
VRS–SO’s concurrent and incremental validity.
Sexual offense variables. All convictions in the present study
were violations of the federal Canadian Criminal Code verified by
fingerprinting and available from the nationwide Canadian Police
Information Centre database. The index offense was defined as the
most recent sexual offense prior to program admission; prior
offenses are those committed before program admission.
Recidivism was defined as any conviction for a new sexual or
nonsexual violent offense following first release to the community
after program participation. A sexual offense is any conviction for
an offense that was clearly sexual in nature (e.g., sexual assault,
sexual interference) or was sexually motivated as determined by
reviewing police reports.7 A nonsexual violent offense was an
offense against a person that was not sexually motivated (e.g.,
nonsexual assault, robbery, uttering threats, murder). The binary
(yes–no recidivism, coded 1– 0) sexual and nonsexual violent
recidivism variables are reported and used in all analyses.

Procedure
Data coding. The VRS–SO dynamic items and Static 99 were
scored by two trained and experienced research assistants by using
archival records extracted from the participant’s institutional paper
files and a computerized Offender Management System.8 The
research assistants made both pre- and posttreatment ratings on the
VRS–SO dynamic items. To guard against potential rater biases,
care was taken to ensure that all pretreatment ratings were blind to
posttreatment and recidivism information. Pretreatment ratings
were generally made up to and including the initial and early
program evaluations, whereas posttreatment ratings were made
incorporating information from the final program evaluation and

321

other available documents such as community prerelease assessments. No interviews were conducted. One of the authors served as
the second rater to assess interrater reliability and only collected
recidivism data after all ratings were completed.
Missing data. Only dynamic items had missing data. No more
than two items were missing from the vast majority of the sample
(91%); the remaining sample had three to four missing items. The
item community support had the most missing data owing to
limited information concerning the release possibility for some
offenders, although it still had complete ratings for 75.4% of the
sample. All missing values were estimated with a regression procedure that can yield accurate estimates of missing values.9 A
potential shortcoming of the procedure is that the resulting mathematically derived estimate may be more accurate than the actual
item score itself (Tabachnick & Fidell, 2001). We also used the
regression procedure to estimate the missing change scores. Rather
than entering a stage of change score for the missing values, we
regressed a missing change score item on the remaining change
score items.
Psychometric analyses. Psychometric analyses of the
VRS–SO included (a) factor analysis of the dynamic items to
identify latent constructs for sexual recidivism risk, (b) descriptive
statistics and comparisons between different sex offender groups,
(c) measurements of interrater reliability and internal consistency,
(d) concurrent validity of the VRS–SO with the Static 99, (e)
predictive validity of the VRS–SO for sexual and nonsexual violent recidivism, and (f) evaluating whether computed change
scores are associated with reduced sexual recidivism.
Four different sets of predictive validity analyses were conducted. First, correlation coefficients (r) and receiver-operating
characteristic analyses were used to investigate the relationships of
6
For instance, a part of the description of the maintenance stage for
deviant sexual preference (D16) is “the individual is now able to effectively suppress or control preferences for deviant sexual stimuli. Evidence
of this includes the identification of external and internal risk factors and
high-risk situations that contribute to sexually deviant preferences and
associated behaviours, as well as adherence to relapse prevention strategies. Other examples may include the reliable use of libido inhibiting
medication . . . , compliance with phallometric sessions, or other reliable
observations” (Wong et al., 2003, p. 61).
7
Official information sources, such as police reports or criminal profile
report in the Offender Management System, were reviewed to ascertain
sexual motivation of an offense primarily for the index conviction(s) for
which the offender was sentenced. In some cases, a recidivist offender
received a federal sentence for a nonsexual violent conviction, and it was
possible to access information concerning the motivation of the recidivistic
offense from Offender Management System as a result. An example would
be an individual who recidivated by committing second degree murder,
although a review of Correctional Service of Canada documents revealed
this to be a sexual homicide.
8
The records included nursing notes, psychological reports, treatment
program summaries and performance evaluations, criminal records, social
histories, community assessments, collateral reports, results of psychological testing, and phallometric assessments.
9
With stepwise multiple regression, the variable with missing data was
regressed on the remaining dynamic items to create a linear combination of
weighted variables. The intercept and unstandardized regression beta
weights were then summed to estimate the value of the missing item for a
specific case.

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

322

OLVER, WONG, NICHOLAICHUK, AND GORDON

the predictors with sexual and nonsexual violent recidivism. The
area under the curve (AUC) statistic in receiver-operating characteristic analyses, unlike r, is independent of changes in base rate
(Rice & Harris, 1995). The AUC (e.g., .70) can be interpreted as
the probability (70% chance) that a randomly selected recidivist
will have a higher score on a given risk measure than will a
randomly selected nonrecidivist (Hanson, 1997). Second, Cox
regression analyses were used to examine the incremental contributions of dynamic variables in predicting sexual recidivism over
and above that made by static variables, including a wellestablished and validated static measure such as the Static 99.
Third, predictive validity was examined through life tables survival analysis (Hanson & Thornton, 1999), which involved measuring the relative cumulative sexual recidivism failure rates of
four VRS–SO risk groups (described below) over a 10-year
follow-up.
Predictive validity was also evaluated through examining the
relationship between risk groups, or “bins,” derived from VRS–SO
total scores with percent sexual recidivism for each of the four
groups. Based, in part, on an examination of the distribution of
VRS–SO total scores and attempts to retain a sufficient number of
offenders in each category, four risk groups were developed: low
(0 –20), moderate-low (21–30), moderate-high (31– 40), and high
(41–72). The percentages of offenders sexually recidivating within
each risk group were computed with fixed 3-, 5-, and 10-year
follow-up windows to test the predictive validity of the VRS–SO
over short-, medium-, and longer term follow-ups, respectively.
Within the duration of a certain fixed follow-up window, offenders
who received a new sexual conviction and those who were offense
free were included in the analyses. Offenders who were at risk for
a shorter duration than the specific follow-up window were not
included.
The change analyses involved computing change scores on the
dynamic items (as outlined in the Method section) and examining
their relationship to sexual reconviction. An inverse relationship
between change and recidivism is predicted (i.e., as positive
change increases, sexual recidivism decreases). Although simple
correlational analyses are an obvious means to test for such a
relationship, a problem with this method is that it does not take into
account that individuals of different risk levels are being compared
on the changes they make. For instance, an offender scoring quite
high on the VRS–SO at pretreatment (such as 50) and registering
a certain amount of change during treatment, would still be at
higher risk for recidivism than would an offender receiving a lower
VRS–SO score (such as 30) and evidencing the same amount of
change. Ideally, a test of the relationship between change and
recidivism should be done separately for each possible score on the
VRS–SO, thereby controlling for level of risk. However, given
pragmatic limitations in sample size, this would not be possible.
Arguably, the best alternative would be to control for risk statistically and thereby examine the relationship of change to sexual
recidivism independent of risk. As such, Cox regression survival
analysis is used to examine the relationship of change to sexual
recidivism while risk as measured by the VRS–SO static scores
and VRS–SO total score (sum of static and dynamic items) is
controlled for. Important advantages of Cox regression survival
analysis are that it takes into account and adjusts for differences in
follow-up time and is less influenced by variations in recidivism
base rate (Hanson, 2006).

Results
Factor Analysis
An exploratory principle components analysis with varimax
rotation was conducted on the pretreatment-rated dynamic items
for the entire sample to identify underlying factors informative to
treatment providers. The scree plot and eigenvalues criteria suggested a three-factor solution which was then extracted with principle axis factoring with Promax oblique rotation (see Tabachnick
& Fidell, 2001, p. 615) to determine whether the derived factors
were correlated. A cutoff loading criterion of .32 was used
(Tabachnick & Fidell, 2001).
The three factors were labeled Sexual Deviance (␣ ⫽ .87),
Criminality (␣ ⫽ .79), and Treatment Responsivity (␣ ⫽ .72).
Their intercorrelations were moderate and significant: Sexual Deviance ⫻ Criminality (r ⫽ .18, p ⬍ .05), Criminality ⫻ Treatment
Responsivity (r ⫽ .55, p ⬍ .001), and Sexual Deviance ⫻ Treatment Responsivity (r ⫽ .38, p ⬍ .001). Table 1 presents the pattern
matrix where the factor loadings represent the unique relationship
between the different variables that compose the three factors. The
intimacy deficits item did not load above the cutoff on any of the
three factors, and the emotional control item had an idiosyncratic
loading pattern (i.e., loading negatively on Treatment Responsivity
and positively on Criminality). As such, these items were not
included in the computation of factor scores, that is, summing the
items loading on their respective factors.

Descriptive Statistics and Between-Groups Comparisons
Table 2 presents descriptive statistics for the total sample and
the different sex offender subtypes for all VRS–SO scale components (including change scores) and the Static 99. Overall group
differences were tested with one-way analyses of variance, and
individual differences with post hoc Tukey beta comparisons (at
Table 1
VRS–SO Dynamic Items Pattern Matrix
Dynamic item
Sexually deviant lifestyle
Deviant sexual preference
Offense planning
Sexual offending cycle
Sexual compulsivity
Impulsivity
Interpersonal aggression
Substance abuse
Compliance with community
supervision
Criminal personality
Community support
Intimacy deficits
Insight
Treatment compliance
Cognitive distortions
Released to high-risk
situations
Emotional control

Sexual
Deviance

Criminality

Treatment
Responsivity

.852
.788
.786
.750
.661
⫺.037
⫺.182
⫺.251

⫺.027
⫺.059
⫺.243
.014
.232
.841
.697
.502

.017
.067
⫺.007
⫺.064
⫺.103
⫺.114
.150
⫺.056

.057
.124
.230
.142
⫺.114
⫺.125
.135

.462
.431
.334
.208
⫺.137
.046
⫺.099

.216
.190
.239
⫺.113
.808
.694
.651

.300
.170

.242
.363

.411
⫺.372

Note. Variables loading on a given factor are highlighted in bold. VRS–
SO ⫽ Violence Risk Scale—Sexual Offender version.

SEX OFFENDER RISK ASSESSMENT

323

Table 2
Means and Standard Deviations of the VRS–SO (Pre-, Posttreatment, and Change Scores) and Static 99 Among Sexual Offender
Subtypes

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Rapist
(n ⫽ 169)

Child molester
(n ⫽ 56)

Mixed
offender
(n ⫽ 45)

Incest offender
(n ⫽ 51)

Total sample
(N ⫽ 321)

Measure

M

SD

M

SD

M

SD

M

SD

M

SD

F
(3, 317)

p

Static 99
VRS–SO static
Dynamic (pre)
Dynamic (post)
Total dynamic change
VRS–SO total (pre)
VRS–SO total (post)
Sexual deviance (pre)
Sexual deviance (post)
Sexual deviance change
Criminality (pre)
Criminality (post)
Criminality change
Treatment responsivity (pre)
Treatment responsivity (post)
Treatment responsivity change

4.7
10.0
24.2
21.5
2.67
34.2
31.5
4.8
4.4
0.42
9.5
8.5
0.97
6.6
5.7
0.93

1.7a
2.9a,b
7.0a,b
6.8
2.17
8.8a,b
8.6
3.4a,b,d
3.1a,b,d
0.69b
3.0
2.8
1.0
2.0
2.1
0.72

4.8
11.6
27.5
24.9
2.59
39.1
36.5
10.1
9.2
0.95
6.9
6.5
0.37
7.1
6.2
0.93

2.0a
3.3a
7.9
7.9
2.01
10.0
9.8
3.8
3.5
0.89
3.8a,c
3.6a,c
0.55a,c
2.2
2.3
0.70

5.5
14.4
28.2
25.6
2.62
42.6
40.0
7.3
6.7
0.62
10.5
9.6
0.90
7.3
6.5
0.84

2.0
2.3
8.6
8.6
2.32
9.6
9.6
3.8b
3.5b
0.73b
3.7
3.5
0.99
2.4
2.4
0.74

2.1
4.4
21.8
19.7
2.13
26.1
24.0
6.4
5.8
0.61
6.8
6.2
0.56
6.5
5.7
0.80

1.6a,b,c
2.2a,b,c
5.6a,b
6.0
1.67
6.4a,b,c
6.6
3.0b
2.9b
0.63b
3.2a,c
3.0a,c
0.72c
1.9
2.1
0.67

4.4
10.0
24.9
22.4
2.56
34.9
32.4
6.3
5.8
0.57
8.8
8.0
0.79
6.8
5.9
0.90

2.0
4.0
7.5
7.4
2.09
10.0
9.9
4.0
3.7
0.75
3.6
3.3
0.93
2.1
2.2
0.71

35.7
110.6
9.3
8.6
0.88
32.8
31.7
34.1
32.6
7.8
19.0
15.7
7.6
2.2
2.2
0.61

⬍.001
⬍.001
⬍.001
⬍.001
ns
⬍.001
⬍.001
⬍.001
⬍.001
⬍.001
⬍.001
⬍.001
⬍.001
ns
ns
ns

Note. All multiple comparisons were computed with Tukey beta post hoc comparisons at p ⬍ .05.
Lower score than mixed offenders.
b
Lower score than child molesters.
c
Lower score than rapists.
d
Lower score than incest offenders.
a

p ⬍ .05) where significant between-groups differences were detected.
The most prominent results emerging from the sex-offendertype comparisons were the differences observed in the Sexual
Deviance and Criminality mean factor scores for the four groups.
Sexual Deviance scores were significantly higher for child molesters than the other three groups. Criminality scores, however, were
significantly higher for rapists and mixed offenders than for the
child molesters and incest offenders. These results are consistent
with what is known about the sexual offending and victim selection characteristics of rapists and child molesters (Blanchard et al.,
2006). In contrast, Static 99 scores were the same for the child
molesters and rapists. The four groups did not differ on the
Treatment Responsivity factor, suggesting that differences in the
Sexual Deviance and Criminality factors did not necessarily lead
to differences in orientation to treatment.
The size of the change scores also appeared to be a function of
the size of pretreatment scores, that is, the larger the pretreatment
score, the larger the change score. Whereas child molesters had the
largest pretreatment Sexual Deviance score and the largest change
score for Sexual Deviance, the rapists and mixed offenders had the
largest Criminality pretreatment and change scores. There were no
differences in total dynamic change scores across the four groups,
given that the differential change scores on Sexual Deviance and
Criminality would have served to cancel each other out when
summed to arrive at a total change score. Overall, the change
scores might seem fairly modest at about 2.5 points (approximately
a third of a standard deviation), which is not an unexpected finding
given the short duration of the program, thus limiting the amount
of change.

The sample can be classified as a medium-high-risk group by
using established Static 99 criteria (Static 99 scores of 4 to 5;
Hanson & Thornton, 1999). Among the four groups, mixed offenders had the highest VRS–SO total and Static 99 scores, and
incest offenders, the lowest.

Interrater Reliability and Internal Consistency
The interrater reliability of the VRS–SO dynamic items and the
Static 99 was assessed on 35 randomly selected cases and evaluated with intraclass correlation coefficients (ICCs). All reported
ICCs (single measure) were significant at p ⬍ .001: Static 99
ICC ⫽ .82; pretreatment dynamic item total ICC ⫽ .74; and
posttreatment dynamic item total ICC ⫽ .79. The interrater reliability of the factor-analytically derived factors were as follows:
Sexual Deviance (pre) ICC ⫽ .72, (post) ICC ⫽ .73; Criminality
(pre) ICC ⫽ .77, (post) ICC ⫽ .80; Treatment Responsivity (pre)
ICC ⫽ .66, (post) ICC ⫽ .73.10 Finally, the reliability of the stages
of change ratings were evaluated by correlating the dynamic item
change scores between the two sets of ratings (r ⫽ .68). The
VRS–SO also demonstrated acceptable internal consistency: dynamic items (pretreatment; ␣ ⫽ .81), static items (␣ ⫽ .67), and
combined scale total (pretreatment; ␣ ⫽ .84).
10
An interrater reliability coefficient could not be calculated for the
VRS–SO static item total, as static scores were computed from numeric
data by using SPSS and were not rated by a human rater. As a result, there
were no initial static ratings made by an independent coder to compare with
those made by a second rater.

OLVER, WONG, NICHOLAICHUK, AND GORDON

324
Concurrent Validity

The VRS–SO was positively correlated (all ps ⬍ .001) with the
Static 99, supporting its concurrent validity: Static total and Static
99 (r ⫽ .70), dynamic total (pre) and (post) with Static 99 (r ⫽ .37
and .35, respectively), and VRS–SO total (pre) and (post) with
Static 99 (r ⫽ .55 and .54, respectively). VRS–SO static and
dynamic item totals were also significantly correlated (pre-r ⫽ .48,
post-r ⫽ .45).

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Prediction of Sexual Recidivism
Recidivism base rates. The sample was followed up for a
mean of 10.0 years (SD ⫽ 4.0; range ⫽ 2.0 to 19.0) following
release. Overall, 79 offenders (24.6%) were convicted for a new
sexual offense, and 115 (35.8%) were convicted for a new nonsexual violent offense. The mean time to first sexual conviction
was 3.8 years (SD ⫽ 3.3; range ⫽ 0.12 to 15.7) and to first
nonsexual conviction, 2.6 years (SD ⫽ 2.7; range ⫽ 0.01 to 13.7).
Predictive accuracy of the VRS–SO and selected risk measures. The VRS–SO static, dynamic, and factor scores, and the
Static 99 (for comparison) were significantly correlated with sexual recidivism and had significant AUC values (see Table 3),
confirmed by none of the 95% confidence intervals for the AUCs
falling below .50.
The three factors of the VRS–SO showed interesting differential
relationships with sexual and nonsexual violence. Whereas the
three VRS–SO factors correlated significantly with sexual recidivism, nonsexual violence was positively correlated with Criminality and negatively correlated with Sexual Deviance. This pattern of
strongly opposing correlations of the two factors within the overall
dynamic domain of the VRS–SO likely accounts for the weak
correlations observed between the dynamic total score and nonsexual violence. The Treatment Responsivity factor predicted both
recidivism criteria. Finally, the VRS–SO static items and the Static
99 each significantly predicted sexual and nonsexual violent re-

cidivism but demonstrated stronger prediction for sexual recidivism.
Incremental contributions of dynamic items to sexual recidivism
prediction. The extent to which the dynamic items made unique
contributions to the prediction of sexual recidivism after the Static
99 and the static items of the VRS–SO were controlled for was
examined through Cox regression survival analysis. The VRS–SO
static item total and the dynamic item total were each entered in
separate steps, with the criterion variable defined as any new
sexual conviction and the time variable defined as time to first
sexual conviction or total follow-up time for nonrecidivists. Significant independent contributions were made by the VRS–SO
static item total, Wald(1) ⫽ 22.64, p ⬍ .001; and dynamic item
total, Wald(1) ⫽ 6.27, p ⫽ .012. Independent contributions were
also observed when these analyses were repeated for the Static 99,
Wald(1) ⫽ 7.76, p ⬍ .01; and the dynamic items, Wald(1) ⫽
18.67, p ⬍ .001.
Survival analysis. Life tables survival analyses were performed to track the relative sexual recidivism failure rates of four
VRS–SO risk groups: low (L) score 0 –20, n ⫽ 39; moderate-low
(ML) score 21–30, n ⫽ 110; moderate-high (MH) score 31– 40,
n ⫽ 107; and high (H) score 41–72, n ⫽ 65, after offenders had
been released into the community and followed up for 10 years
(see Figure 1). Significant differences in failure rate were observed
among the four risk groups overall, ␹2(3) ⫽ 36.51, p ⬍ .001, and
in pairwise comparisons. The H group failed at a higher and faster
rate than did the MH, ␹2(1) ⫽ 5.25, p ⬍ .05; ML, ␹2(1) ⫽ 25.06,
p ⬍ .001; and L, ␹2(1) ⫽ 18.87, p ⬍ .001, groups. The MH group
had higher failure rates compared with the ML, ␹2(1) ⫽ 9.70, p ⬍
.01; and L, ␹2(1) ⫽ 10.24, p ⬍ .01, groups, and no significant
difference was observed between the ML and L groups, ␹2(1) ⫽
2.60, p ⫽ .107. Despite having VRS–SO scores of up to 20, the L
group failed at an extremely low rate even up to 10 years.
Percent sexual recidivism and risk level. The percentage of
individuals who sexually recidivated was computed for each of the
four VRS–SO risk groups or “bins” over fixed 3-, 5-, and 10-years

Table 3
Predictive Accuracy of the VRS–SO Scale Components (Pre- and Posttreatment) and Static 99 for Sexual and Nonsexual
Violent Recidivism
Sexual recidivism
Measure
Static 99
VRS–SO static
Dynamic (pre)
Dynamic (post)
Pretreatment total
Posttreatment total
Sexual deviance (pre)
Sexual deviance (post)
Criminality (pre)
Criminality (post)
Treatment responsivity (pre)
Treatment responsivity (post)

r
***

.21
.36***
.23***
.26***
.32***
.34***
.14*
.16**
.17**
.21***
.12*
.15**

AUC
**

.63
.74***
.66***
.67***
.71***
.72***
.59*
.61**
.63***
.65***
.58*
.59**

Nonsexual violent recidivism
95% Cl
.56,
.68,
.59,
.60,
.64,
.66,
.52,
.54,
.56,
.58,
.51,
.52,

.70
.80
.73
.74
.77
.78
.66
.68
.70
.72
.65
.66

r

AUC
*

.13
.17**
.03
.06
.09
.11*
⫺.26***
⫺.25***
.26***
.28***
.15*
.15**

*

.57
.60**
.53
.55
.56
.57*
.35***
.35***
.65***
.67***
.59**
.60**

Note. N ⫽ 321. VRS–SO ⫽ Violence Risk Scale—Sexual Offender version; AUC ⫽ area under the curve statistic; CI ⫽ confidence interval.
p ⬍ .05. ** p ⬍ .01. *** p ⬍ .001.

*

95% Cl
.51,
.53,
.46,
.48,
.50,
.51,
.29,
.29,
.59,
.61,
.53,
.53,

.64
.66
.59
.61
.62
.64
.41
.41
.71
.73
.65
.65

SEX OFFENDER RISK ASSESSMENT

325

1.1

Cumulative proportion surviving

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

1.0
.9
.8
.7
.6
.5

VRS–SO Risk Level

.4

High (41+)

.3

Mod-High (31–40)

.2

Mod-Low (21–30)

.1

Low (0–20)
0

2

4

6

8

10

12

Survival time (years) to first sex offense
Figure 1. Survival analysis. Cumulative sexual recidivism failure rates as a function of Violence Risk
Scale—Sexual Offender version (VRS–SO) risk level over a 10-year follow-up. Mod ⫽ moderate.

follow-up (Figure 2).11 VRS–SO risk groups (L, ML, MH, and H)
were found to be significantly associated with sexual recidivism at
each follow-up window: 3 years, ␹2(3) ⫽ 33.85, ␸ ⫽ .33, p ⬍
.001; 5 years, ␹2(3) ⫽ 34.31, ␸ ⫽ .34, p ⬍ .001; and 10 years,
␹2(3) ⫽ 36.31, ␸ ⫽ .44, p ⬍ .001.

Evaluating the Relationship of Change to Reductions in
Sexual Recidivism
Aggregate change scores for each participant were computed by
summing the individual item change scores for all 17 dynamic
items. Total change scores ranged from –2.50 to ⫹10.13 points of
change (M ⫽ 2.56, SD ⫽ 2.09), and for Sexual Deviance, – 0.50 to
3.50 (M ⫽ 0.57, SD ⫽ 0.75), Criminality, –1.0 to 6.50 (M ⫽ 0.78,
SD ⫽ 0.93), and Treatment Responsivity, –1.50 to 3.03 (M ⫽ 0.90,
SD ⫽ 0.71).12
Cox regression survival analyses were conducted to examine the
relationship of therapeutic change to sexual recidivism while risk
and follow-up time were controlled for. In the first analysis, the
VRS–SO static score and total dynamic change score were entered
as covariates, and both variables demonstrated significant unique
relationships to sexual recidivism: VRS–SO static score,
Wald(1) ⫽ 41.33, p ⬍ .001, exp(B) ⫽ 1.230; total dynamic
change, Wald(1) ⫽ 3.94, p ⬍ .05, exp(B) ⫽ 0.896. In the second
analysis, the VRS–SO pretreatment total score (i.e., combined
static and dynamic to further control for risk) and total dynamic
change score were entered as covariates. Both variables also demonstrated significant relationships to sexual recidivism and in the
expected direction: VRS–SO pretreatment total, Wald(1) ⫽ 42.99,
p ⬍ .001, exp(B) ⫽ 1.070; total dynamic change, Wald(1) ⫽ 4.42,

p ⬍ .05, exp(B) ⫽ 0.900. Exp(B) is an odds ratio statistic representing the predicted change in hazard (e.g., risk for recidivism)
per unit increase in the predictor (see Fox, 2002; Tabachnick &
Fidell, 2001). Exp(B) values exceeding 1 indicate that higher
scores on a measure are associated with increased recidivism, and
values less than 1, with decreased recidivism. An exp(B) of 0.900,
for instance, would be interpreted to mean that for every 1-point
increase in change score (i.e., a unit of positive change), there
would be a predicted 10% decrease in sexual recidivism after
accounting for risk. In sum, whereas scores on the risk measures
maintained a significant association with increased recidivism,
therapeutic change scores made significant and unique contributions to sexual recidivism prediction. In other words, reductions in
VRS–SO dynamic scores, an indication of improvement during
treatment, were found to be related to reductions in sexual recidivism after measurements of risk and follow-up time were controlled for. In the present sample of sex offenders, a seemingly
modest average change score of approximately 2.5 (see Table 2)
could be extrapolated to mean a predicted 25% reduction in sexual
recidivism with treatment after accounting for risk.
We further examined the relationship of therapeutic change to
sexual recidivism as a function of risk by dividing the offender
sample into broad high- (n ⫽ 204) and low- (n ⫽ 117) risk groups
11

See Figure 2 note for ns within each bin.
As a result of using the regression procedures (described in the
Procedure subsection) to estimate, or prorate, missing data, some of the
reported values are not whole numbers or in denominations of 0.5. Negative change scores indicate the offender deteriorated in treatment.
12

OLVER, WONG, NICHOLAICHUK, AND GORDON

326

Low (0–20)

Mod-Low (21–30)

Mod-High (31–40)

High (41–72)

80

70

Percent sexual reconviction

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

70
60

50
50

41.9
40

34.3

30

24.2
20

17

20

8.1

10

2.6

4.6

6.7

3

0
3 years

5 years

10 years

Figure 2. Base rates of sexual recidivism at derived Violence Risk Scale—Sexual Offender version (VRS–SO)
risk categories over fixed 3-, 5-, and 10-year follow-up periods. Group bin sizes for each follow-up period were
as follows: 3 years, N ⫽ 317, low n ⫽ 38, moderate (mod)-low n ⫽ 109, moderate-high n ⫽ 106, high n ⫽ 64;
5 years, N ⫽ 293, low n ⫽ 33, moderate-low n ⫽ 99, moderate-high n ⫽ 99, high n ⫽ 62; 10 years, N ⫽ 187,
low n ⫽ 15, moderate-low n ⫽ 70, moderate-high n ⫽ 62, high n ⫽ 40.

on the basis of their Static 99 scores (MH- and H-risk groups were
collapsed into a single high-risk group, and ML- and L-risk groups
were collapsed into a single low-risk group). The distributions of
therapeutic change scores for each group were broadly normal and
fell within acceptable limits of skewness and kurtosis. Cox regression analyses were performed separately for each group. Within
the high-risk group, significant independent relationships were
observed for total dynamic change, Wald(1) ⫽ 4.28, p ⬍ .05,
exp(B) ⫽ 0.882; and the VRS–SO static items, Wald(1) ⫽ 21.23,
p ⬍ .001, exp(B) ⫽ 1.215. However, within the low-risk group, no
relationship was observed for total dynamic change, Wald(1) ⫽
0.005, ns, exp(B) ⫽ 1.01; although the static items continued to
predict sexual recidivism, Wald(1) ⫽ 11.11, p ⫽ .001, exp(B) ⫽
1.31. As dichotomizing the groups by risk level is a crude means
of controlling for risk, it is worth noting that consistent with the
above Cox regression analyses of the high- and low-risk groups,
change scores were significantly negatively correlated with sexual
recidivism in the high-risk group (r ⫽ –.15, p ⬍ .05) but not the
low-risk group (r ⫽ .01, ns) nor with the entire group (r ⫽ –.09,
p ⫽ .10). The results demonstrate therapeutic change to be more
predictive (and hence, likely more informative) among higher risk
sex offenders.

Discussion
The VRS–SO was designed to integrate within a single instrument the assessment of sex offender risk, the identification of
dynamic variables linked to sex offending as treatment targets, and
the evaluation an offender’s treatment readiness and change; in

short, to develop a sex offender risk assessment tool that can also
inform and guide sex offender treatment. The present study investigated the psychometric properties of the VRS–SO in a sample of
treated sex offenders, including its reliability, concurrent and predictive validity, and the relationship of changes in the dynamic
variables to changes in sexual recidivism.

Psychometric Properties of the VRS–SO
A factor analysis of the dynamic items suggested that the
dynamic component of the scale (17 of 24 variables) can be
summarized by three factors labeled Sexual Deviance, Criminality,
and Treatment Responsivity. Two of the three factors extracted are
consistent with the two major sex offender risk factor domains
(i.e., sexual deviance, antisocial orientation) that have been demonstrated to predict sexual recidivism in the empirical literature
(e.g., Doren, 2004; Hanson & Morton-Bourgon, 2004). Specifically, Sexual Deviance taps a constellation of variables that include deviant sexual interests, lifestyle, and preoccupations. Criminality comprises variables that reflect a generalized antisocial
lifestyle. The Treatment Responsivity factor reflects distorted attitudes and beliefs supportive of sexual offending and resistance to
change and would likely entail a lack of compliance with change
agents such as sex offender treatment.
Of note is that scores on the Sexual Deviance and Criminality
factors indicated differences among sex offender types. Consistent
with empirical findings (e.g., Pithers, 1993), rapists and mixed
offenders tended to be a more criminalized group of offenders,
scoring highest on the Criminality factor, whereas child molesters

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

SEX OFFENDER RISK ASSESSMENT

were more likely to have deviant preferences (e.g., Blanchard et
al., 2006) and scored highest of the four groups on the Sexual
Deviance factor. Clinically, assessing sex offenders with the
VRS–SO can provide a criminogenic profile based on scores on
the three dynamic factor domains that can provide differential
information to inform sex offender treatment and case conceptualization. As a result, treatment resources, usually in short supply,
can be better focused onto the most salient problem areas as
indicated by the broad factor domains and then onto specific
treatment targets, as indicated by the individual dynamic items, to
increase treatment efficacy.
The VRS–SO has acceptable interrater reliability (.66 to .80),
even when raters were required to rate fairly difficult dynamic
items from file information. Acceptable internal consistency was
also obtained for the dynamic item total (␣ ⫽ .81) and the three
dynamic factor domains (␣s ranging from .72 to .87).

Concurrent and Predictive Validity
VRS–SO static, dynamic, total, and factor scores each significantly predicted sexual recidivism. VRS–SO total scores also
predicted sexual recidivism over short-, medium-, and long-term
follow-ups. The relationship of the different VRS–SO factors to
nonsexual violent recidivism was also consistent with what one
would expect from a perusal of the literature (Hanson & MortonBourgon, 2004). Although Criminality was significantly positively
correlated with nonsexual violent recidivism, Sexual Deviance was
significantly negatively correlated with this outcome.
The concurrent validity of the VRS–SO was examined with
respect to the Static 99, a widely used static actuarial risk measure
developed and validated on a large international sample of sex
offenders (Hanson & Thornton, 1999). The VRS–SO static items
had a substantially higher correlation (r ⫽ .70) with the Static 99
than did the VRS–SO dynamic items (r ⫽ .37), suggesting that
there is limited overlap between the dynamic items and the Static
99 and raising the possibility that the dynamic items may capture
sexual recidivism variance not explained by the Static 99. This
possibility was tested with Cox regression, and the results demonstrated that the total dynamic score made significant incremental
contributions to predicting sexual recidivism over and above that
of the Static 99. The dynamic items also showed incremental
validity for sexual recidivism prediction beyond the VRS–SO
static items.

Are the VRS–SO Dynamic Variables Indeed Dynamic?
To evaluate whether the dynamic items are indeed dynamic, we
examined the relationship of changes in the VRS–SO dynamic
items to changes in sexual recidivism. The results supported the
contention that positive changes in the dynamic items, indicating a
reduction in risk, are related to reductions in sexual recidivism.
Change scores suggested a significant unique inverse relationship
with sexual recidivism after statistically controlling for actuarial
risk by using Cox regression survival analysis. The results can be
interpreted in practical terms to mean for every 1-point increase in
change score (i.e., one unit of positive change), there would be a
predicted 10% decrease in sexual recidivism after accounting for
risk. It follows that, in the present sample, with an average total
change score of approximately 2.5, there would be a predicted

327

25% overall reduction in the probability of sexual recidivism with
treatment after accounting for risk. This is not a trivial effect in
terms of reducing sexual revictimization, and the magnitude of this
anticipated reduction is broadly consistent with meta-analytic sex
offender treatment outcome findings (Hanson et al., 2002) as well
as prior treatment outcome research on a similar Clearwater sample (Nicholaichuk, Gordon, Gu, & Wong, 2000). The present
results are consistent with the notion that the VRS–SO dynamic
items are dynamic and that the dynamic items and their change
scores are potentially informative of treatment change and risk
reduction. However, a cross-validation of the present findings on a
different sample of treated sex offenders is necessary to evaluate
the generalizability of these results.
Not all dynamic items, however, are equally dynamic. Some
items, such as sexually deviant lifestyle, may be more resistant to
change than is an item such as cognitive distortions. Given the
substantial correlation (r ⫽ .48) between the static and dynamic
items, it could be argued that the VRS–SO dynamic and static
items share considerable common variance, and therefore, some
dynamic items may be more like static items. We would argue that
the static and dynamic items are more like two sides of the same
coin. For instance, an individual with many dynamic items endorsed is expected to be more criminalized and sexually deviant
and, therefore, more likely to transgress social and legal boundaries and run afoul of the law, the consequences of which (e.g.,
sexual assault) are captured by the static items. As such, the static
and dynamic items would be expected to be moderately or even
quite highly correlated. Whether one focuses on the functional
characteristics of the individual (the dynamic items) or the lifetime
outcomes of his or her antisocial behaviors (static items) is the
choice one makes in looking at one or the other side of the same
coin. Treatment providers would be well advised to pay attention
to dynamic items to capture clinically relevant information about
the client presenting for treatment.
The results also demonstrated that dynamic item change scores
are more closely linked to sexual recidivism in higher risk than
lower risk offenders, as found when change analyses were conducted separately in the two risk groups through Cox regression.
The results are not unexpected and suggest that not only is there
more room to change for higher risk offenders, but changes made
in treatment (or lack thereof) are potentially more informative in
sexual recidivism risk assessment for this group of offenders.
In conclusion, the results of the present study provide preliminary empirical support for the use of the VRS–SO as a sex
offender risk assessment and prediction tool that can also be useful
for treatment purposes. The tool uses dynamic items to inform
treatment planning and delivery and to measure changes in risk
that, as the data suggest, are linked to changes in sexual recidivism.
It is our hope that continued efforts to validate the use of dynamic
variables to assess risk and evaluate therapeutic change will lead to
improvements in the assessment and treatment of sex offenders
and, ultimately, the reduction of sexual victimization.

Limitations and Future Directions
Although the present study has generated some promising findings to support the validity and reliability of the VRS–SO, there
are limitations that may be redressed through future research. First,
the static items were developed and cross-validated on separate

OLVER, WONG, NICHOLAICHUK, AND GORDON

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

328

halves of the sample, but the entire sample was used in all
prediction analyses, which could result in inflating the predictive
validity correlations of the static items. Yet, on the other hand, a
more conservative estimate of the relationship of change to sexual
recidivism may have been obtained as a result of statistically
controlling for a static measure with potentially inflated prediction.
Second, the Static 99 was the only sex offender risk assessment
tool used as a comparison measure with the VRS–SO in the current
study. Future research could compare the VRS–SO against other
sex offender risk measures. Third, as data were collected by using
only file information, a replication of the study with both file and
interview is warranted. Conducting interviews, in turn, should
increase the volume of clinical information available for making
VRS–SO ratings and thus potentially enhance the validity and
reliability of the assessment.
As this is the first major study investigating the psychometric
properties of the VRS–SO, future research efforts would be well
served to cross-validate the tool prospectively on a different sample of sex offenders to increase the generalizability of the current
findings. Of particular interest may be further research examining
the relationship of changes in the dynamic items to reductions in
sexual recidivism and how therapeutic movement on these variables can inform supervision, risk management, and decision making with sex offenders.

References
Andrews, D. A., & Bonta, J. (2003). The psychology of criminal conduct
(3rd ed.). Cincinnati, OH: Anderson.
Beech, A., Friendship, C., Erikson, M., & Hanson, R. K. (2002). The
relationship between static and dynamic factors and reconviction in a
sample of U.K. child abusers. Sexual Abuse: A Journal of Research and
Treatment, 14, 155–167.
Blanchard, R., Kuban, M. E., Blak, T., Cantor, J. M., Klassen, P., &
Dickey, R. (2006). Phallometric comparison of pedophilic interest in
nonadmitting sexual offenders against stepdaughters, biological daughters, other biologically related girls, and unrelated girls. Sexual Abuse: A
Journal of Research and Treatment, 18, 1–14.
Boer, D. P., Hart, S. D., Kropp, P. R., & Webster, C. D. (1997). Manual for
the Sexual Violence Risk–20: Professional guidelines for assessing risk
of sexual violence. Vancouver, British Columbia, Canada: Institute
against Family Violence and the Mental Health, Law, and Policy Institute, Simon Fraser University.
Browne, A., & Finkelhor, D. (1986). Impact of sexual abuse: A review of
the research. Psychological Bulletin, 99, 66 –77.
Doren, D. M. (2004). Towards a multidimensional model for sexual
recidivism risk. Journal of Interpersonal Violence, 19, 835– 856.
Douglas, K. S., & Kropp, P. R. (2002). A prevention-based paradigm for
violence risk assessment: Clinical and research applications. Criminal
Justice and Behavior, 29, 617– 658.
Douglas, K. S., & Skeem, J. (2005). Violence risk assessment: Getting
specific about being dynamic. Psychology, Public Policy, and Law, 11,
347–383.
Epperson, D. L., Kaul, J. D., & Hesselton, D. (1998). Minnesota Sex
Offender Screening Tool—Revised (MnSOST–R): Development, performance, and recommended risk level cut scores. St. Paul, MN: Iowa State
University and Minnesota Department of Corrections.
Fox, J. (2002). Cox proportional-hazards regression for survival data. In J.
Fox (Ed.), An R and S-PLUS companion to applied regression (pp.
1–18). Thousand Oaks, CA: Sage.
Hanson, R. K. (1997). The development of a brief actuarial scale for sexual
offense recidivism (User Report 97– 04). Ottawa, Ontario, Canada: Department of the Solicitor General of Canada.

Hanson, R. K. (2005, June). The assessment of criminogenic needs of
sexual offenders by community supervision officers: Reliability and
validity. Symposium conducted at the 66th annual meeting of the Canadian Psychological Association, Montreal, Quebec, Canada.
Hanson, R. K. (2006). Does Static 99 predict recidivism among older
sexual offenders? Sexual Abuse: A Journal of Research and Treatment,
18, 343–355.
Hanson, R. K., & Bussière, M. T. (1998). Predicting relapse: A metaanalysis of sexual offender recidivism studies. Journal of Consulting and
Clinical Psychology, 66, 348 –362.
Hanson, R. K., Gordon, A., Harris, A. J. R., Marques, J. K., Murphy, W.,
Quinsey, V. L., & Seto, M. C. (2002). First report of the Collaborative
Data Outcome Project on the effectiveness of psychological treatment
for sexual offenders. Sexual Abuse: A Journal of Research and Treatment, 14, 169 –194.
Hanson, R. K., & Harris, A. J. R. (2000). Where should we intervene?
Dynamic predictors of sexual offense recidivism. Criminal Justice and
Behavior, 27, 6 –35.
Hanson, R. K., & Harris, A. J. R. (2001). A structured approach to
evaluating change among sexual offenders. Sexual Abuse: A Journal of
Research and Treatment, 13, 105–122.
Hanson, R. K., & Morton-Bourgon, K. (2004). Predictors of sexual recidivism: An updated meta-analysis (User Report 2004 – 02). Ottawa, Ontario, Canada: Public Safety and Emergency Preparedness Canada.
Hanson, R. K., & Thornton, D. (1999). Static 99: Improving actuarial risk
assessments for sex offenders (User Report 99 – 02). Ottawa, Ontario,
Canada: Department of the Solicitor General of Canada.
Harris, G. T., Rice, M. E., Quinsey, V. L., Lalumière, M. L., Boer, D., &
Lang, C. (2003). A multi-site comparison of actuarial risk instruments
for sex offenders. Psychological Assessment, 15, 413– 425.
Hart, S. D., Kropp, P. R., & Laws, D. R. (2003). The Risk for Sexual
Violence Protocol (RSVP)—Structured professional guidelines for assessing risk of sexual violence. Burnaby, British Columbia, Canada:
Mental Health, Law and Policy Institute, Simon Fraser University.
Hudson, S. M., Wales, D. S., Bakker, L., & Ward, T. (2002). Dynamic risk
factors: The Kia Marama evaluation. Sexual Abuse: A Journal of Research and Treatment, 14, 103–119.
Johnson, H., & Sacco, V. F. (1995). Researching violence against women:
Statistics Canada’s national survey. Canadian Journal of Criminology,
37, 281–304.
Kraemer, H. C., Kazdin, A. E., Offord, D. R., Kessler, R. C., Jensen, P. S.,
& Kupfer, D. J. (1997). Coming to terms with the terms of risk. Archives
of General Psychiatry, 54, 337–343.
Levesque, D. A., Gelles, R. J., & Velicer, W. F. (2000). Development and
validation of a stages of change measure for men in batterer treatment.
Cognitive Therapy and Research, 24, 175–199.
Lisak, D., & Miller, P. M. (2002). Repeat rape and multiple offending
among undetected rapists. Violence and Victims, 17, 73– 84.
McGrath, R. J., & Hoke, S. E. (2002). Vermont Assessment of Sex Offender
Risk manual: Research edition 2001. Waterbury, Vermont: Author.
Nicholaichuk, T. P., Gordon, A., Gu, D., & Wong, S. (2000). Outcome of
an institutional sexual offender treatment program: A comparison between treated and matched untreated offenders. Sexual Abuse: A Journal
of Research and Treatment, 12, 139 –153.
Pithers, W. D. (1990). Relapse prevention for sexual aggressors: A method
for maintaining therapeutic gain and enhancing external supervision. In
W. L. Marshall, D. R. Laws, and H. E. Barbaree (Eds.), Handbook of
sexual assault: Issues, theories, and treatment of the offender (pp.
343–361). New York: Plenum Press.
Pithers, W. D. (1993). Treatment of rapists: Reinterpretation of early
outcome data and exploratory constructs to enhance therapeutic efficacy.
In G. C. N. Hall, R. Hirschman, J. R. Graham, & M. S. Zaragoza (Eds.),
Sexual aggression: Issues in etiology, assessment, and treatment (pp.
167–196). Philadelphia, PA: Taylor & Francis.

This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

SEX OFFENDER RISK ASSESSMENT
Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. (1992). In search of
how people change: Applications to the addictive behaviors. American
Psychologist, 47, 1102–1114.
Proulx, J., Pellerin, B., Paradis, Y., McKibben, A., Aubut, J., & Ouimet, M.
(1997). Static and dynamic predictors of recidivism in sexual aggressors.
Sexual Abuse: A Journal of Research and Treatment, 9, 7–27.
Quinsey, V. L., Rice, M. E., & Harris, G. T. (1995). Actuarial prediction
of sexual recidivism. Journal of Interpersonal Violence, 10, 85–105.
Rice, M. E., & Harris, G. T. (1995). Violent recidivism: Assessing predictive
validity. Journal of Consulting and Clinical Psychology, 63, 737–748.
Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics.
Boston, MA: Allyn and Bacon.
Thornton, D. (2002). Constructing and testing a framework for dynamic
risk assessment. Sexual Abuse: A Journal of Research and Treatment,
14, 139 –153.
Tierney, D. W., & McCabe, M. P. (2005). The utility of the trans-

329

theoretical model of behavior change in the treatment of sex offenders.
Sexual Abuse: A Journal of Research and Treatment, 17, 153–170.
Ward, T., & Hudson, S. M. (1998). A model of the relapse process in sex
offenders. Journal of Interpersonal Violence, 13, 700 –725.
Willoughby, T., & Perry, G. P. (2002). Working with violent youth:
Application of the transtheoretical model of change. Canadian Journal
of Counseling, 36, 312–326.
Wong, S., & Gordon, A. E. (2000). The Violence Risk Scale. Unpublished
manuscript.
Wong, S. C. P., & Gordon, A. E. (2006). The validity and reliability of the
Violence Risk Scale: A treatment-friendly violence risk assessment tool.
Psychology, Public Policy, and Law, 12, 279 –309.
Wong, S., Olver, M. E., Nicholaichuk, T. P., & Gordon, A. (2003). The
Violence Risk Scale—Sexual Offender version (VRS–SO). Saskatoon,
Saskatchewan, Canada: Regional Psychiatric Centre and University of
Saskatchewan.

Appendix
VRS–SO Static and Dynamic Items and Brief Item
Descriptions
Static Items
S1. Age at release: ⬍ age 25 years; 25–34 years; 35– 44
years; 45 years and up
S2. Age at first sex offense: ⬍ age 20 years; 20 –24 years;
25–34 years; 35 years and up
S3. Sex offender type: mixed offender; child molester;
rapist; incest offender
S4. Prior sex offenses: 4 or more prior sexual charges/
convictions; 2–3 prior; 1 prior; 0 prior
S5. Unrelated victims: 4 or more unrelated victims; 2–3
unrelated; 1 unrelated; 0 unrelated (all related)
S6. Victim gender: 2⫹ male victims; 1 male and 1 female/or 2⫹ female; 1 male victim only; 1 female victim only
S7. Prior sentencing dates: 11⫹ prior sentencing dates;
5–10 prior; 2– 4 prior; 0 –1 prior

Dynamic Items
D1. Sexually deviant lifestyle: lifestyle hobbies, interests,
work, or relationships involve sexually deviant behaviors
D2. Sexual compulsivity: strong sex drive and high frequency of sexual behavior and cognitions
D3. Offense planning: victim grooming and premeditation
involved in sexual offending
D4. Criminal personality: interpersonal and emotional attributes conducive to criminal behavior (e.g., lack of remorse)
D5. Cognitive distortions: attitudes and distorted thinking
supportive of sexual offending
D6. Interpersonal aggression: physically and/or verbally
aggressive behavior in interpersonal interactions

D7. Emotional control: tendency to overcontrol or undercontrol emotions linked to sexual offending
D8. Insight: poor understanding of causes of sexual offending and unwillingness to discuss/explore sexual offending
D9. Substance abuse: substance use problems linked specifically to sexual offending
D10. Community support: lack of positive support people,
services, or plans in community (or unwilling to use)
D11. Released to high-risk situations: offender seems
likely or has shown pattern of returning to situations linked to sex
offending
D12. Sexual offending cycle: pattern of interpersonal, situational, and personal factors linked to sexual offending
D13. Impulsivity: behavior displays tendency to “act first,
think later” and lacks reflection or forethought
D14. Compliance with community supervision: poor attitude and/or cooperation with community supervision
D15. Treatment compliance: poor attitude and/or cooperation with sex offender treatment
D16. Deviant sexual preference: interests or preferences for
deviant sexual stimuli or behaviors (e.g., children, violence)
D17. Intimacy deficits: incapacity to form or maintain adult
romantic relationships
Note. All items are rated on a 4-point (3, 2, 1, 0) scale. Item
descriptions are abbreviated examples of the originals and are not
intended to be used for clinical or research purposes. Please
consult the VRS–SO rating manual (Wong, Olver, Nicholaichuk,
& Gordon, 2003) for more detailed item descriptions, stages of
change ratings, and scoring instructions.
Received December 19, 2006
Revision received June 5, 2007
Accepted June 6, 2007 䡲