The Sex Offender Need
Assessment Rating (SONAR): A Method for
Measuring Change in Risk Levels 2000-1
By
R. Karl Hanson &
Andrew Harris Corrections Research Department of the Solicitor General of Canada
The risk assessment procedures contained in
this report, including SONAR, have been developed by the authors in
the course of their duties. Anyone choosing to use or adopt the risk
assessment procedures, including SONAR in any way, does so on the
sole basis of their responsibility to judge their suitability for
their own specific purposes. The Department of the Solicitor General
Canada, its employees, agents, servants and the authors neither
assume nor accept any responsibility or legal liability for any
injury or damages whatsoever resulting from the use of the risk
assessment procedures and SONAR.
The views expressed are those of the authors
and do not necessarily reflect the views of the Department of the
Solicitor General Canada. This document is available in French. Ce
rapport est disponible en français sous le titre: L'échelle d'évaluation des besoins des
délinquants sexuels (SONAR): Une méthode permettant de mesurer le
changement de niveau de risque..
Correspondence concerning this article should
be addressed to R. Karl Hanson, Ph.D., Corrections Research,
Department of the Solicitor General Canada, 340 Laurier Avenue West,
Ottawa, Ontario, Canada K1A 0P8. E-mail: hansonk@sgc.gc.ca.
Public Works and Government Services Canada
Cat. No.: JS42-88/1999E
ISBN: 0-662-28407-0
Abstract
Presently, there are
no established scales that could be used to evaluate change in risk
among sexual offenders. The Sex Offender Need Assessment Rating
(SONAR) was developed to fill this gap. The SONAR includes five
relatively stable factors (intimacy deficits, negative social
influences, attitudes tolerant of sex offending, sexual
self-regulation, general self-regulation) and four acute factors
(substance abuse, negative mood, anger, victim access). The
psychometric properties of the scale were examined using data
previously collected by Hanson and Harris (1998, in press). Overall,
the scale showed adequate internal consistency and moderate ability
to differentiate between recidivists and non-recidivists (r = .43;
ROC area of .74). SONAR continued to distinguish between the
groups after controlling for well-established risk indicators, such
as age, IQ, and scores on the Static-99 (Hanson & Thornton,
1999) and the Violence Risk Appraisal Guide (VRAG; Quinsey et al.
1998).
The Sex Offender
Need Assessment Rating (SONAR):
A method for
measuring change in risk levels
The evaluation of recidivism risk is
important for the management of sexual offenders and recent years
have seen considerable advances in risk assessment. A number of
offender characteristics, such as sexual deviance and criminal
lifestyle, have been reliably linked with recidivism risk (Hanson
& Bussière, 1998) and several specialised risk scales have been
developed (Quinsey, Harris, Rice & Cormier, 1998; Epperson, Kaul
& Hesselton, 1998; Hanson, 1997a; Hanson & Thornton, 1999,
in press). Although the existing scales can be useful for evaluating
long-term risk potential, they are poor measures of change. Most of
the items on the extant scales are static, historical items.
Consequently, these scales are of little help for many risk
decisions, such as whether an offender has benefited from treatment,
or whether he should be suspended from conditional release.
Evaluating change requires variables capable
of changing, i.e., dynamic variables (Bonta, 1996). Although age is
sometimes considered a dynamic variable, the most useful dynamic
variables are those that are amenable to deliberate intervention.
Dynamic variables can be further subdivided into stable risk
factors, which would be expected to persist for months or years
(e.g., personality disorders, alcoholism), and acute risk factors,
which may last for days or only minutes (e.g., intoxication, acute
anger).
The relatively low recidivism rates of sexual
offenders makes it difficult to detect dynamic risk factors. Over a
4-5 year period, approximately 10-15% of sexual offenders will be
detected committing a new sex offence (Hanson & Bussière, 1998).
Only static or highly stable factors can be expected to predict
recidivism many years later. In order to focus on potentially
dynamic risk factors, Hanson and Harris (1998, in press) examined
the antecedents of recidivism in a group of sexual offenders already
known to have reoffended while on community supervision. Comparisons
with non-recidivists identified a number of dynamic risk factors,
such as non-cooperation with supervision, victim access, anger,
sexual pre-occupations, and acute changes in mood.
The current study examines how well the
dynamic risk factors identified in the Hanson and Harris (1998)
study can be organised into a structured risk assessment. The
construction of this new scale, the Sex Offender Need Assessment
Rating (SONAR), was guided by theory as well as by the findings of
the Hanson and Harris (1998) study. This study cannot claim to
establish the predictive validity of the measure because the same
data base was used to develop items and to test the scale's
validity. Instead, the study had the more modest aim of suggesting a
plausible approach to dynamic risk assessment, an approach that is
sufficiently explicit to be used and evaluated in other samples.
The development of the SONAR was guided by
social cognitive theory (e.g., Bandura, 1977; Fiske & Taylor,
1991) as has been applied to general criminal behaviour (e.g.,
Andrews & Bonta, 1998) and sexual offending (Johnson & Ward,
1996; Laws, 1989). In this model, recidivistic sexual offenders
would be expected to hold deviant schema, or habitual patterns of
thought and action, that facilitate their offences. The likelihood
that an offender will invoke such schema would increase if the
schema were well rehearsed, were triggered by common circumstances,
were considered socially acceptable, and were consistent with the
offender's personality and values. Each offender's crime cycle would
be somewhat unique. Nevertheless, certain characteristics would be
expected to provide fertile ground for the development and
maintenance of deviant sexual schema.
The SONAR items are divided into five stable
factors (intimacy deficits, negative social influences, attitudes
tolerant of sexual offending, sexual self-regulation, general
self-regulation) and four acute factors (substance abuse, negative
mood, anger, victim access). The scoring criteria are given in
Appendix I. The rationale for the inclusion of each of these
constructs is described below.
Intimacy deficits
The importance of intimacy deficits for
sexual offenders has been supported by several lines of research
(Marshall, 1993; Ward, Hudson & McCormack, 1997). In contrast to
the relationships of trust associated with normal sexuality, social
interactions connected with sexual offending are, by definition,
problematic. Sexual offenders often report little satisfaction from
their intimate relationships (Seidman, Marshall, Hudson, &
Robertson, 1994), lack empathy for women (Hanson, 1997b), and pursue
sex in uncommitted relationships (Malamuth, 1998). Sex offenders who
have never been married are at increased risk for recidivism (Hanson
& Bussière, 1998) and offenders with severe courtship disorders
(see Freund, Seto, & Kuban, 1997) appear to be at particularly
high risk for recidivism. Frisbie (1969), for example, reported that
"grave difficulties in establishing meaningful relationships with
adult females"(p. 163) was one of the most important predictors of
sexual offense recidivism. As well, Hanson and Bussière (1998) found
that the closer the pre-existing relationship with the victim, the
lower the recidivism rate (incest < acquaintances <
strangers).
Social influences
Among general criminal populations, the
number of criminal companions is one of the strongest predictors of
recidivism (Gendreau, Little & Goggin, 1996). Research has yet
to examine the link between negative peer associates and sexual
recidivism. Such a link is plausible, however, given that sex
offenders are likely to have friends and relatives who are also
sexual offenders (Hanson & Scott, 1996). In the case of
pro-pedophilia organizations (e.g., Thorstad, 1991), the social
support for sex offending can be explicit. In most cases, however,
the social influences are likely to have an indirect influence on
sex offending through promoting generally antisocial attitudes, poor
behavioural controls, substance abuse, and dysfunctional coping
strategies. Peers who support the offenders' denial or facilitate
victim access would also be considered to be poor social influences.
Attitudes
Attitudes or values tolerant of sexual
assault are also plausibly related to sex offence recidivism. Among
community samples, there is consistent evidence that men who admit
to sex offending also endorse "rape myths" or attitudes that condone
such behaviour (Dean & Malamuth, 1997; Malamuth, Sockloskie,
Koss & Tanaka, 1991). Research with convicted sexual offender
samples has been less consistent, but there is some evidence that
deviant sexual attitudes are common among both child molesters and
rapists (Bumby, 1996; Hanson, Gizzarelli & Scott, 1994).
Averaged across four studies (n = 439), Hanson and Bussière's (1998)
meta-analysis found a small positive correlation between deviant
sexual attitudes and sexual offence recidivism, a finding that has
been replicated in subsequent research (Bakker, Hudson, Wales &
Riley, 1999).
Sexual
self-regulation
One of the most distinctive risk factors for
sexual offenders is a problem with sexual self-regulation. Sexual
offenders perceive themselves to have strong sexual urges, and feel
entitled to act out their sexual impulses (Hanson et al., 1994). Sex
is overvalued in the pursuit of happiness. Sexual offenders believe
that sexual activity (normal or otherwise) increases their social
status (Kanin, 1967) and mitigates life stress (Cortoni, 1998).
According to relapse prevention theory, a
common trigger for sexual offending is negative mood or stress
(Pithers, Beal, Armstong & Petty, 1989). The overall level of
subjective distress does not appear to be important in predicting
recidivism (Hanson & Bussière, 1998). What does seem important,
however, are the mechanisms used by sex offenders for regulating
their emotional and sexual feelings. Research has found, for
example, that sexual offenders are most likely to engage in deviant
sexual fantasies following stressful events (McKibben, Proulx &
Lusignan, 1994; Proulx, McKibben & Lusignan, 1996). Sexual
offenders would be expected to be at high risk to reoffend if a)
many circumstances, including negative affect, arouse sexual
imagery; and b) they feel deprived or frustrated if they are unable
to quickly satisfy their sexual urges.
General
self-regulation
In addition to problems with emotional/sexual
self-regulation, offenders may also have problems with general
self-regulation. Impulsive behaviour is so common among offenders
that some theorists have proposed that "low self-control" is the
essential element of all criminal behaviour (Gottfredson &
Hirshi, 1990). Offenders tend to smoke, drink excessively, use
drugs, drive fast, quit school, and engage in multiple short-term
sexual relationships beginning at an early age. Scales used to
predict criminal recidivism, such as the Hare Psychopathy Checklist
- Revised (PCL-R, Hare et al., 1990; Hare, 1991) or Level of Service
Inventory - Revised (LSI-R, Andrews & Bonta, 1995), typically
contain numerous items related to impulsivity and lifestyle
instability. In general, factors related to general criminality also
predict sexual offence recidivism among sex offender samples (Hanson
& Bussière, 1998).
Although sexual offenders may have fewer
problems with lifestyle instability than other offender groups, poor
behavioural controls can, nevertheless, directly contribute to
sexual offending. Some offenders impulsively commit sex offences
given the opportunity (e.g., an encounter with a vulnerable female
victim during the course of a burglary). Poor self-control can also
have an indirect influence on recidivism among those with an
established pattern of sexual deviance. Self-management skills are
required in order to conform to the demands of treatment and
community supervision, and to sustain long-term life changes.
Acute risk factors
In addition to the stable risk factors
described above, the SONAR also considers a number of acute risk
factors. Acute risk factors are not necessarily related to long-term
recidivism potential; instead, they are useful in identifying when sex offenders are most likely to
reoffend. The four acute risk factors included in the SONAR were the
following: a) substance abuse, b) negative mood (e.g., depression,
anxiety), c) anger/hostility, and d) opportunities for victim
access. These four items were selected because they were
significantly related to recidivism in the Hanson and Harris (1998)
data set and were not already addressed by the stable risk factors
included in SONAR.
The SONAR subscales were created from the
individual questions in the Hanson and Harris (1998, in press) data
set that most closely matched the constructs of interest. For some
constructs, the connection between the indicators and the construct
had high face validity (e.g., negative peer influences); for other
constructs, the connection was less obvious (e.g., sexual
self-regulation). When multiple indicators were available, an
attempt was made to retain only the minimum number of items
necessary to reliably sample the domain. The inclusion of individual
items was guided by face validity and observed differences between
the recidivistic and non-recidivistic offenders.
Method
The data used to test the SONAR was the same
as that reported by Hanson and Harris (1998, in press). Since the
data collection procedures have already been described elsewhere,
only a brief overview of the research method will be provided.
Interested readers are referred to the original reports (Hanson
& Harris, 1998, in press).
Subject Selection
The study considered non-incestuous, hands-on
sexual offenders who had received community supervision (parole,
probation) from the Canadian provincial or federal correctional
systems. The offenders were divided into 208 who committed a new
sexual offence while on community supervision and 201 who had not
recidivated with a sexual offence or serious violent offence. The
offenders were further divided into approximately equal numbers of
boy-victim child molesters (n = 122) girl-victim child molesters (n
= 150) and rapists (n = 137). For each offender type, the
recidivists and non-recidivists were matched on offence history,
index victims, and jurisdiction. On average, the non-recidivists had
completed 24 months in the community, whereas most of the
recidivists had re-offended within 15 months.
File Review
Variables
A standardised coding manual was used to
record background information for each case. This information was
based on complete file reviews and national criminal history records
obtained from the Royal Canadian Mounted Police. The background
information included basic identifying information, demographics,
psychological assessments (e.g., intelligence, mental disorders),
detailed sexual offence histories, and a number of other variables
related to recidivism risk.
Violence Risk
Appraisal Guide (VRAG). (Quinsey et al., 1998).
Originally developed to predict violent
recidivism among offenders referred to a maximum security
psychiatric institution (Harris, Rice & Quinsey, 1993), the VRAG
has attracted considerable interest as an actuarial predictor of
violence (Borum, 1996). Its 12 tems include Hare's Psychopathy
Checklist - Revised (Hare, 1991), other personality disorders, early
school maladjustment, age, marital status, criminal history,
schizophrenia, and victim injury. An application of the VRAG to a
replication sample of 159 sexual offenders (Rice & Harris, 1997)
found that it correlated .47 with violent recidivism (sexual and
nonsexual violence). Due to incomplete files, VRAG scores were
available for 146 recidivist and 121 non-recidivists.
Static-99 (Hanson
& Thornton, 1999, in press).
Static-99 was designed to predict sexual
recidivism using a limited number of easily scored items. The
Static-99 items were drawn from Hanson's (1997a) Rapid Risk
Assessment for Sexual Offense Recidivism (RRASOR; prior sex
offences, male victims, unrelated victims, age less than 25) and
Thornton's Structured Anchored Clinical Judgement (SAC-J; index
nonsexual violence, prior nonsexual violence, 4+ sentencing dates,
single, any stranger victims, any non-contact sex offences - see
Grubin, 1998). Across a combined sample of
1,208 sex offenders from four different
settings, Static-99 correlated .33 with sex offence recidivism and
.32 with any violent recidivism (Hanson & Thornton, 1999).
Interview
Variables
Most of the information used to create SONAR
was drawn from one-hour, structured interviews with the supervision
officers. Officers indicated whether particular problems had ever
been a concern during the whole course of supervision, and, if so,
whether the problem was worse at T1 or T2. For the recidivists, T2
was the month preceding the recidivism event and T1 was a
within-subject control period six months earlier. For the
non-recidivists, T2 was simply the preceding month of supervision.
For each time period (ever, T1, T2) officers rated each risk factor
as '0 - no, never a problem', '1 - very slight or possible problem
or concern', or '2 - yes, some problem'.
The interviews contained 128 individual items
organised into 22 content areas. These topic areas included
substance abuse, mood, psychiatric symptoms, attitudes tolerant of
sexual assault, lifestyle instability, sexual preoccupations, and
cooperation with supervision. The complete list of factors is
available in Hanson and Harris (1998). The subset of items used in
the construction of the SONAR are listed in Appendix I.
Procedure
The data were collected by four field
researchers working under the supervision of the project manager
(Andrew Harris). Each field researcher received a week of group
training, on-site supervision during their first week in the field,
and an additional 1-2 weeks of on-site supervision during the course
of data collection.
Interviews with the supervision officers were
conducted in the officers' usual place of work during normal office
hours.
The field researchers coded the file material
before or after the interview depending on the availability of the
officer. The file coding was based on all available information and
typically took 3-5 hours. The researcher who coded the files also
conducted the corresponding interview.
Reliability
Approximately 10% of the cases (43) were
coded separately by two raters in order to estimate reliability. The
inter-rater reliability was consistently high for all coders in the
study. The average percent agreement was 95% for the static file
coding, 97% for interview ratings, 94% for supervision case notes.
Results
As can be seen in Table 1, the sampling
procedure successfully matched the recidivists and non-recidivists
on a number of static variables, including marital status, race,
index victim type and the number of previous sexual offences.
Nevertheless, differences on static variables remained. The
recidivists were more likely than the non-recidivists to have
diverse types of victims, paraphilias, prior non-sexual offences,
lower IQ, and to meet PCL-R definitions of psychopathy (20% versus
8%). The recidivists were also higher risk than the non-recidivists
on established risk scales: Violence Risk Appraisal Guide (VRAG) (r
= .35, p < .001); and Static-99 (r = .15, p < .01). The groups
did not differ on the RRASOR since the cases were explicitly matched
on the main variables in this scale.
The items of the SONAR were moderately
intercorrelated (alpha = .67). In the total sample, SONAR scores
ranged from -3 to 14, with a mean of 6.7 (SD = 3.1). The recidivists
had higher scores than the non-recidivists on the total score and
each of the subscales (see Table 2). The average SONAR score for the
recidivists was 8.0 (SD = 2.4, range 1 to 14) compared to an average
score of 5.4 for the non-recidivists (SD = 3.1, range -3 to 12). The
ability of the scale to distinguish between the groups was
moderately high (r = .43; ROC area of .74).
As can be seen in Table 3, SONAR scores
correlated with age (r = -.15, p < .01), intelligence (r = -.15,
p < .01), and Static-99 scores (r = .14, p < .01). It also
showed a substantial correlation with the VRAG (r = .39, p <
.001), suggesting that SONAR items are at least partially addressing
an enduring propensity for violent behaviour. In support of this
interpretation, the SONAR items that correlated most strongly with
the VRAG were the stable items, particularly general self-regulation
(r = .40, p < .001).
The next set of analyses considered the
extent to which SONAR scores continued to differentiate the groups
after controlling for pre-existing risk factors. These analyses were
conducted using logistic regression (see Neter, Kutner, Nachtsheim
& Wasserman, 1996) since the outcome variable was dichotomous
(recidivist, non-recidivists) and the unstandardised logistic
regression coefficients remain constant across various distributions
of the independent (i.e., risk scores) or dependent (i.e.,
recidivism base rates) variables.
When entered alone in logistic regression,
SONAR has a regression coefficient of .35 (SD = .043, Wald = 64.31,
p < .001). The exponent of the coefficient, e(B) , is an odds ratio. In this case, B
equals .35, e is the constant 2.718, which yields an odds ratio of
(2.718)(.35) = 1.42. Given low base
rates, the odds ratio can be interpreted as a rate ratio (the
recidivism rate of the more deviant group divided by the recidivism
rate of the less deviant group). For each point increase in SONAR
scores, the recidivism rate would be expected to increase by 42%.
If, for example, the recidivism rate of offenders with SONAR scores
of '7' was 20%, offenders with scores of '8' would be expected to
recidivate at 28.4% (20% x 1.42 = 28.4%).
Table 1
Comparison of the
recidivists and non-recidivists on static, historical variables
Measure |
Recidivists |
Non-recidivists |
Sig |
Sample size |
208 |
201 |
|
Age at exposure to risk |
36.3 (11.2) |
39.1 (11.6) |
<.05 |
Ever married (%) |
59.2 |
62.8 |
Ns |
Minority race (%) |
14.0 |
11.5 |
Ns |
Predominant victim type (n)
adult women (rapists)
boys
girls |
71
61
76 |
66
61
74 |
|
Diverse victim types (%) |
53.8 |
33.3 |
<.001 |
Number of paraphilias (voyeurism, exhibitionism,
fetishes, etc.) |
1.5 (1.5) |
1.0 (1.1) |
<.001 |
Prior sentencing occasions
for sex offences
for any offences |
1.3 (1.8) 5.3
(5.3) |
1.1 (1.4)
4.1 (5.8) |
Ns
<.05 |
IQ |
94.4 (14.6) |
100.1 (14.5) |
<.001 |
PCL-R Psychopathy
|
23.4 (6.8)
20.5
10.9 (8.6) |
16.7 (8.7)
8.0
4.3 (9.0) |
<.001
<.001 |
VRAG
sample size |
146 |
121 |
|
RRASOR |
2.6 (1.3) |
2.3 (1.3) |
Ns |
Static-99 |
4.8 (1.8) |
3.4 (1.9) |
<.01 |
Note. Standard
deviations in parentheses
Table 2
Comparison of the
recidivists and non-recidivists on SONAR items.
Measure |
Recidivists |
Non-recidivists |
r |
SONAR total
score
|
8.0 (2.4) |
5.4 (3.1) |
.43*** |
Stable total |
7.6 (1.9) |
5.7 (2.5) |
.40*** |
Intimacy deficits |
1.7 (0.6) |
1.5 (0.7) |
.10* |
Negative social influences |
1.2 (0.8) |
0.7 (0.8) |
.30*** |
Attitudes |
1.7 (0.7) |
1.2 (0.9) |
.31*** |
Sexual self-regulation |
1.7 (0.5) |
1.3 (0.7) |
.31*** |
General self-regulation |
1.7 (0.5) |
1.2 (0.6) |
.41*** |
Acute total |
.39 (1.4) |
-.38 (1.5) |
.26*** |
Substance abuse |
.06 (.43) |
-.11 (.48) |
.19*** |
Negative mood |
.05 (.69) |
-.09 (.81) |
.10* |
Anger/hostility |
.10 (.45) |
-.10 (.52) |
.20*** |
Victim access |
.18 (.55) |
-.08 (.54) |
.23*** |
When the control variables of age, IQ,
Static-99 and VRAG were considered in the subsample for whom
complete information was available (n = 228), the regression
coefficient for SONAR was .32 (sd = .061, Wald = 27.19, p <
.001). This can be interpreted to mean that even after controlling
for some well-established static risk indicators, each increase in
SONAR scores corresponds to an expected recidivism rate increase of
38% (e(.32) = 1.38;
95% confidence interval of 1.22 to 1.55). The only other
variables that remained significant in the multivariate
prediction were the VRAG (B = .055; Wald = 6.49, p < .05) and IQ
(B = -.027, Wald = 5.22, p < .05).
Figure 1 illustrates the combined ability of
the VRAG and SONAR to distinguish between the recidivists and
non-recidivists. For the offenders with low VRAG scores (-1 or less)
and below average SONAR scores (less than 7), only 14% were
recidivists (5 of 35). For offenders with low VRAG scores but above
average SONAR scores, 57% (12 of 21) were recidivists. Similarly,
the proportion of recidivists among the offenders with high VRAG
scores (14 +) depended on whether their SONAR scores were below
average (43%, 9 of 21) or above average (86%, 47 of 55).
Table 3
Correlation of SONAR
items with age, IQ, and recidivism risk.
|
Age at
release |
IQ |
Static-99
|
VRAG
|
Sample size |
409 |
316 |
409 |
267
|
SONAR Total |
-.15** |
-.15* |
.14** |
.39*** |
Stable Total |
-.12* |
-.13* |
.16** |
.42*** |
Intimacy deficits |
-.05 |
-.14* |
.07 |
.13* |
Negative social influences |
-.14** |
-.16** |
.04 |
.33*** |
Attitudes |
-.08 |
-.05 |
.15** |
.36*** |
Sexual self-regulation |
-.03 |
-.02 |
.13** |
.30*** |
General self-regulation |
-.10* |
-.09 |
.17** |
.40*** |
Acute Total |
-.10* |
-.11 |
.05 |
.14* |
Substance abuse |
-.11* |
-.07 |
.09 |
.07 |
Negative mood |
-.03 |
-.08 |
-.02 |
.03 |
Anger/hostility |
-.09 |
-.10 |
.01 |
.05 |
Victim access |
-.06 |
-.02 |
.07 |
.23*** |
* p < .05; ** p
< .01; *** p < .001
Discussion
The aim of the present study was to present a
risk scale that could be used to evaluate change in risk among
sexual offenders. The scale included both stable factors (intimacy
deficits, social influences, attitudes, sexual self-regulation,
general self-regulation) and acute factors (substance abuse, mood,
anger, victim access). The properties of the scale were examined
using data previously collected by Hanson and Harris (1998, in
press). Overall, the scale showed adequate internal consistency and
moderate ability to differentiate between recidivists and
non-recidivists (r = .43; ROC area of .74). All of the SONAR items
differentiated the groups, with the strongest effects for problems
with general self-regulation. SONAR continued to distinguish between
the groups after controlling for well-established risk indicators,
such as age, IQ, and scores on Static-99 (Hanson & Thornton,
1999) and the Violence Risk Appraisal Guide (VRAG; Quinsey et al.
1998).
The relatively strong effect for general
self-regulation deficits is consistent with Quinsey, Coleman, Jones,
and Altrows' (1997) finding that "dynamic antisociality" predicted
reoffending among mentally disordered offenders supervised in the
community. For all types of offenders, low self-control may play a
central role in violation of conditional release. In fact, violation
of conditional release is commonly included on measures of
recidivism risk, such as the LSI-R (Andrews & Bonta, 1995) and
the PCL-R (Hare, 1991). The relative importance of general
self-regulation deficits in the current study may also be
attributable to the ease with which these behaviours were observed
by the community supervision officers. The officers may know little
about the offenders' intimate relationships, but the officers had
considerable opportunity to observe the offenders' behaviour in
supervision.
Although SONAR's predictive accuracy was
respectable, the results need to be interpreted cautiously. The
extent to which the results will generalise is unknown since the
same data set was used to develop and test the items. A more serious
qualification, however, is that most of the information was drawn
from interviews with community supervision officers who were fully
aware of which offenders had recidivated or not. Consequently, the
findings were vulnerable to retrospective recall biases. Minor
events may acquire new significance after the reoffense is known. It
is unlikely, however, that the results can completely be attributed
to recall biases as the major risk factors (e.g., anger, sexual
preoccupations) were also recorded in the case notes written prior
to the recidivism event being known (see Hanson & Harris, in
press). The available case notes lacked sufficient detail to score
SONAR items, so reliance on interview data was necessary.
All the SONAR items were intended to be
dynamic, but it is possible that they are proxies for enduring
propensities. The SONAR was substantially correlated with static
measures of risk, such as the VRAG, and the stable items in the
SONAR carried most of the predictive accuracy. Nevertheless,
reported changes in the acute risk factors signalled changes in
recidivism risk even after controlling for the strongest static and
stable risk factors available in the data set (see Hanson &
Harris, 1998). The extent to which changes in SONAR scores indicate
changes in recidivism risk will only be known given repeated
assessments in truly prospective studies.
The results suggest that dynamic factors are
important in risk assessment, but the current study does not support
any direct translation of SONAR scores into expected recidivism
rates. The observed proportion of recidivists in each risk category
obviously does not correspond to recidivism rates since the study
used an artificial base rate of 50%. In most applied settings, the
sex offence recidivism rate would be expected to be considerably
lower - approximately 5% per year (see Hanson & Thornton, 1999).
Even the highest annual rates rarely exceed 15%-20% (high risk
offenders in the first year after release).
Statistically minded readers may be tempted
to adjust the results of the current study to recidivism base rates
appropriate for their specific assessment context. Indeed, the
information provided is sufficient to construct logistic regression
equations that estimate recidivism probabilities for any combination
of SONAR, IQ, VRAG and recidivism base rate. We do not believe,
however, that the design of the study was strong enough to support
such detailed predictions. The design was retrospective and the
findings from truly prospective studies may not be the same.
A related question is the extent to which
SONAR variables should be used to adjust the actuarial predictions
provided by other actuarial instruments, such as Static-99 or the
VRAG. Some authors, such as Quinsey et al. (1998), have argued
against adjusting actuarial scales on the grounds that clinical
opinion is so poor that adjustments only dilute the valid
predictions provided by the actuarial scale. The accuracy of
actuarial scales, however, depends on the extent to which they
address all the relevant risk factors. The results of the current
study suggest that the predictions provided by many of the common
risk scales, such as the RRASOR, Static-99, and the VRAG, can be
improved by considering a range of dynamic risk factors related to
behaviour while on community supervision. The best method of
incorporating this information is unknown, but some adjustment seems
justified when the offenders' dynamic needs are substantially higher
(or lower) than would be expected from their scores on established
actuarial measures.
Despite the study's limitations, the risk
factors included in the SONAR are sufficiently consistent with
previous research to be considered plausible risk indicators in
applied contexts. It is hoped that such dynamic factors will be
considered by evaluators in their risk assessments, and by treatment
providers as potential treatment targets. By systematically
addressing the problems suggested by the SONAR, future research will
be able to determine the extent to which these dynamic factors are
important in the risk management of sexual offenders.
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Appendix I
SONAR scoring criteria
Stable items |
Score
|
Intimacy deficits |
0 - current lover, no troubles
1 - current lover, troubles
2 - no current lover |
|
Social influences |
0 - positive social balance of 2+
1 - balance of 0 or +1
2 - balance less than zero |
|
Attitudes |
0 = no agreement with any
1 = agrees with some
2 = agrees with many |
|
Sexual Self-Regulation |
0 = no entitlement or preoccupations
1 = some entitlement or some sexual
preoccupations
2 = strong entitlement or
3+ sexual preoccupations |
|
General Self-regulation |
0 = no problem
1 = some problem
2 = serious problem |
|
Acute risk factors |
Substance abuse |
-1 = better 0 = same 1 = worse |
|
Negative mood |
-1 = better 0 = same 1 = worse |
|
Anger/hostility |
-1 = better 0 = same 1 = worse |
|
Opportunities for Victim access |
-1 = fewer 0 = same 1 = more |
|
Total |
|
Unless otherwise
specified, the time period addressed by the stable risk factors is
the preceding 12 months.
Intimacy Deficits
If the offender has no current lover, then he
receives a score of "2". If the offender is living with a current
lover, and there are no obvious troubles, then he receives a score
of "0". If he is living with a current lover, but the relationship
is conflicted or problematic, then he receives a score of "1".
Potential problems could include affairs/infidelity, sexual
problems, distrust, jealousy, general conflicts, and long-term
separations (e.g., prison). The degree of troubles should be
sufficient to be of concern to the man or his partner. A score of
"1" would also be given to stable dating relationships that do not
involve living together.
Social Influences
Name all the people in the offender's life
who are not paid to be with him. For each one, is the influence
positive, negative or neutral?
The number of positive influences minus the
number of negative influences equals the social balance. Recode
social balance: (2+ = 0) (0, 1 = 1) (less than 0 = 2).
Attitudes
Would the offender agree with the following
statements?
Rape Attitudes:
Score as follows: 0 = no; 1 = maybe,
somewhat; 2 = yes.
- Many women would secretly like to be raped
- When women go around wearing short skirts
or tight tops they are asking for trouble
- A lot of times when women say "no" they
are just playing hard to get and really mean "yes"
- That women are playing with him sexually
- That some rape victims deserve what they
get
RECODE Rape: (0 = 0) (1, 2, 3, 4 = 1) (5 - 10
= 2)
Child Molesting
Attitudes:
Score as follows: 0 = no; 1 = maybe,
somewhat; 2 = yes.
- Some children are mature enough to enjoy
sex with adults
- Some children like to sexually tease him
- A child who does not resist sexual
touching really feels OK about being touched
- Some children are so willing to have sex
that it is difficult to stay away from them
RECODE Child Molest: (0 = 0) (1, 2, 3 = 1) (4
- 8 = 2).
RECODE Total: 0 = no agreement with any; if
Rape or Child Molest = 1, then Total = 1; if Rape or Child
Molest = 2, then Total = 2.
Emotional/Sexual
Self-Regulation
This need area concerns poorly controlled
expression of sexual impulses and the tendency to use sexuality as a
method of coping with negative emotions. The tendency to use
sexuality as a coping mechanism was not directly measured in Hanson
and Harris (1998). Instead, this dimension included indirect
measures of sexual deviancy, such as sexual entitlement and sexual
preoccupations.
Would the offender
agree with the following statements (Sexual Entitlement)?
Score as follows: 0 = no; 1 = maybe,
somewhat; 2 = yes.
- Everyone is entitled to sex
- Men need sex more than women do
- He has a higher sex drive than most people
- Once they get you wound-up sexually, you
just can't stop
RECODE Sexual Entitlement: 0 = 0, 1 - 3 = 1,
4+ = 2.
Has the offender
engaged in any of the following (Sexual Preoccupations)? Scores as follows: 0 = no, 1 = maybe, 2 = yes.
- Pornography use
- Strip bars/massage parlours/prostitutes
- Lusty talk
- Excessive masturbation
- Deviant sexual fantasies/urges
- Preoccupation with sex crimes
- Preoccupation with sex/porno/hookers
RECODE Sexual Pre-occupations: 0 = 0, 1 - 4 =
1, 5+ = 2.
RECODE TOTAL Sexual Self-Regulation: 0 = no
entitlement or preoccupations; 1 = Entitlement or Sexual
Preoccupations of 1; 2 = Entitlement or Sexual Preoccupations of 2.
General
Self-Regulation
This need area concerns the offender's
ability to self-monitor and conform to the demands of community
supervision. Offenders with generally criminal lifestyles would be
expected to have problems in this area.
Has the offender been?
Score as follows: 0 = no, 1 = maybe, 2 = yes,
except reversed items that are scored 0 = yes, 1 = maybe, 2 = no.
- Testing known risk factors
- Keeping secrets
- Invested in treatment (Reversed)
- Trying to "play the system"
- Trying to be "buddy-buddy with you"
- Breaking conditions of community
supervision
- Failing to attend commitments other than
community supervision
- Willing to make sacrifices to avoid high
risk situations (Reversed)
RECODE (0 = 0) (1 - 7 = 1) (8 - 16 = 2).
For each of the following four problem areas,
consider whether the offender's behaviour has improved (-1),
deteriorated (+1), or remained the same (0) during the past month
(or since the last assessment).
A) Substance abuse problems (alcohol and drugs).
Look for interference in normal daily
activities and/or health problems.
B) Negative mood
- depression/discourage/hopeless
- anxiety/excessive worry/stress
- frustration
- loneliness
- suicidal thoughts
C) Anger/hostility
- flying off the handle/volatility/anger
- anger towards women
- any aggressive/rude/threatening to
others
D) Victim access/grooming
- access to victims (general)
- cruising/creating opportunities to
reoffend
- grooming of victims
- bicycle/4X4/motorcycle/flashy car (Does
the offender have a vehicle that would be expected to attract the
attention of his preferred victim type?)
- computer/surf the net
- hobbies: camera/fishing/kites/boats (Does
the offender engage in a hobby that would be expected to
facilitate contact with his preferred victim type?)
Sum the four items (A, B, C, D) and then add
(or subtract) from the stable dynamic items.
Translating SONAR
scores into risk categories
Category |
SONAR Score |
Low |
-4 to 3 |
Low moderate |
4, 5 |
Moderate |
6, 7 |
High moderate |
8, 9 |
High |
10 - 14 |
|