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CRIMINAL JUSTICE AND BEHAVIOR
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OFFENDER RISK ASSESSMENT
Guidelines for Selection and Use
JAMES BONTA
Solicitor General Canada
During the past 20 years, there have been significant developments in the area of offender
assessment. As a result, this knowledge has placed the field in a position to construct guidelines as to what should characterize useful and effective offender assessment instruments. The
author’s suggestions as to what constitutes good assessment ranges from the noncontroversial
(e.g., actuarial instruments) to the more contentious (e.g., lessening one’s reliance on static
risk scales). Whether the reader agrees with the views expressed, it is hoped that the force of
the empirical arguments will at least provoke some careful consideration rather than summarily
dismissing them.
F
or those who have to deal with offenders, either directly or indirectly, conducting assessments is simply part of their regular work
routine. Any intervention with an offender requires an assessment of
how the characteristics of the offender and the situation are related to a
relevant outcome. Within a correctional context, there are many outcomes of interest. A correctional officer, for example, may need to
judge whether a depressed inmate is suicidal. Parole board members
consider the likelihood of an inmate adjusting to life in the community. A therapist must assess an offender’s progress in treatment. The
list can go on, but these examples are sufficient to demonstrate that
offender assessments are not only common activities but also that the
AUTHOR’S NOTE: The opinions expressed are those of the author and do not necessarily represent the views of the Ministry of the Solicitor General of Canada. Correspondence concerning this article should be addressed to James Bonta, Corrections
Research, Solicitor General Canada, 340 Laurier Ave. W., Ottawa, Ontario, Canada,
K1A 0P8; e-mail: [email protected].
CRIMINAL JUSTICE AND BEHAVIOR, Vol. 29 No. 4, August 2002 355-379
© 2002 American Association for Correctional Psychology
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results from these evaluations are important to correctional staff,
offenders, and the community.
In this article, I summarize what we know about offender risk
assessment and suggest to practitioners some guidelines for the selection and use of risk instruments. Given that the reduction of criminal
behavior is one of the major goals of most correctional systems, my
focus is on the assessment of risk for recidivism. The article is not
intended to provide a critical review of the many psychological tests
and offender classification instruments used in the field. Granted, we
can use such a review, and many of the articles in this issue provide
evaluations of some offender assessment instruments. The research
literature is sufficiently robust to offer general suggestions about what
should constitute good correctional assessment. Thus, this article is
outlined as a set of guidelines along with a supporting rationale. Some,
but I hope not too many, will disagree with the suggestions and the
rationale for them. Perhaps some of them are plainly wrong (and my
colleagues tell me it would not be the first time that I was wrong).
However, I hope that most of what I present is reasonable or at least
debatable on empirical grounds.
GUIDELINE 1: ASSESSMENT OF OFFENDER RISK
SHOULD BE BASED ON ACTUARIAL MEASURES OF RISK
Here we are in the 21st century, and one would think that we have
resolved the debate between clinical and actuarial approaches to risk
assessment. Judging from what goes on in the field, I do not think we
have. The results from two recent surveys are the source of my concern. Boothby and Clements (2000) asked 830 correctional psychologists what specific psychological tests they used in their work.
Twenty-three percent reported using the Bender-Gestalt, 20% used
the Rorshach, and 14% used projective drawings. Although there are
“scoring systems” for these tests, I do not think that they can be considered actuarial. Actuarial measures are structured, quantitative, and
empirically linked to a relevant criterion. To the best of my knowledge, there is no credible literature demonstrating that these tests predict criteria important to corrections (i.e., prison assaults, escape, suicide, and, of course, recidivism).
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The Boothby and Clements (2000) survey asked psychologists
what tests they used. We do not know why they used them. Maybe they
were used solely for purposes other than risk assessment (e.g., diagnosis of a neurological impairment or psychiatric disorder). I suspect
not. Gallagher, Somwaru, and Ben-Porath (1999) asked directors of
mental health services in 25 state correctional systems what psychological tests were used in preparole evaluations. With preparole
assessments, risk should be a major consideration. The results were
dishearteningly informative. Not surprisingly, the Minnesota
Multiphasic Personality Inventory (MMPI) and the revised version,
MMPI-2, were used in nearly all the states (96%). However, the Rorschach and Bender-Gestalt were used in 36% of the states, HouseTree-Person in 28%, and Human Figure Drawing in 20% of the
jurisdictions.
Where are the actuarial risk scales that predict recidivism?
Boothby and Clement (2000) reported that three instruments specifically developed for offenders were infrequently used. The Psychopathy Checklist–Revised (PCL-R) (Hare, 1990) was cited by 11% of
the psychologists surveyed and the Level of Service Inventory–
Revised (LSI-R) (Andrews & Bonta, 1995) and the Violence Risk
Appraisal Guide (VRAG) (Harris, Rice, & Quinsey, 1993) by less
than 1%. These three instruments are probably the best validated
offender risk measures available today. If so few correctional psychologists use them, and undoubtedly risk assessment is an important role
for correctional psychologists, then what are they using for risk
assessments? Perhaps psychologists use the results from actuarial risk
scales administered by other professionals (e.g., the LSI-R), or they
are relying on clinical judgment.
There is ample evidence that actuarial assessments of risk are significantly superior to clinical assessments (see Grove & Meehl, 1996,
for a general review of the research). This is true even for such diverse
samples as mentally disordered offenders (Bonta, Law, & Hanson,
1998) and sex offenders (Hanson & Bussière, 1998). In predicting
violent recidivism among mentally disordered offenders, the average
effect size (r) was .09 for clinical assessments of risk and .30 for actuarial assessments (Bonta et al., 1998). Similar findings were reported
for sex offenders by Hanson and Bussière (1998). Clinical assess-
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ments of the risk for sexual recidivism yielded an r of .10, whereas
actuarial assessments produced an r of .46.
The point does not have to be labored: The weight of the evidence
clearly favors actuarial assessments of risk. Some (Grove & Meehl,
1996; Quinsey, Harris, Rice, & Cormier, 1998) have gone so far as to
suggest that not using actuarial instruments may be unethical and
unprofessional. A common defense of the clinical approach is to say
“It is not ‘either/or’: I use both.” However, as Grove and Meehl (1996)
have noted, when the results from the two approaches disagree, a
choice must be made. One cannot use both.
GUIDELINE 2: RISK ASSESSMENTS SHOULD
DEMONSTRATE PREDICTIVE VALIDITY
One of the unique skill sets of psychologists, compared to many
other human service professionals, lies in their knowledge of
psychometric theory. Psychologists love developing and using tests.
One can see psychologists come alive when the discussion turns to the
internal reliability of a scale or its factor structure. Tests should evidence face validity and good internal and interrater reliability, and one
hopes for a stable underlying factor structure, convergent and
discriminative validity, and so forth. All of these are important
psychometric characteristics of good tests. However, we sometimes
lose sight of the most important type of validity: predictive validity.
Many of the clinical measures used by correctional psychologists,
such as the MMPI/MMPI-2 and the Millon Clinical Multiaxial Inventory (MCMI), have relatively few studies on their predictive validity
with offender populations. Most of the research deals with other
psychometric issues. For example, with the publication of the MMPI2, almost all the published research has dealt with the comparability of
the new version with the original MMPI (Helmes & Reddon, 1993). In
the correctional and forensic areas, the research has included, for
example, the construct validity of the Antisocial Practices Scale
(Lilienfeld, 1996), equivalence to the Megargee MMPI-based classification system (Megargee, 1994), construction of norms (Shea, McKee,
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Craig Shea, & Culley, 1996), and cross-sectional (not longitudinal)
validation studies (Heilbrun & Heilbrun, 1995). The picture is much
the same with the Millon inventories (see, e.g., McCann & Dyer,
1996).
There are some risk assessment instruments for specific use with
offender populations, but at this time, they have very little data on their
predictive validity. For example, the HCR-20 (Webster, Douglas,
Eaves, & Hart, 1997) is a promising assessment tool measuring historical, clinical, and potential risk management factors. At the point of
writing (end of 2001), there were three published studies on the predictive validity of the HCR-20. One study (Grann, Belfrage, &
Tengström, 2000) used only the historical component of the HCR-20,
whereas the other two studies (Douglas, Ogloff, Nicholls, & Grant,
1999; Kroner & Mills, 2001) used all three components. All three
studies found satisfactory predictive accuracy with the HCR-20.
However, the reader is reminded that the Grann et al. (2000) study
investigated only one component of the HCR-20 (with results opposite to a retrospective study by Strand, Belfrage, Fransson, &
Levander, 1999). In addition, the Douglas et al. (1999) study used a
sample of civilly committed psychiatric patients. Only the Kroner and
Mills (2001) study used a correctional population. Correctional and
forensic professionals must exercise caution in the use of instruments
that are in the early developmental stages of research.
Obviously, a risk instrument must be validated on its ability to predict criminal behavior. Many of the assessment instruments used by
psychologists have been developed to predict outcomes that are not
particularly critical to corrections. Neither the MMPI/MMPI-2 nor
the MCMI was developed to assess the risk of reoffending. I will not
even comment on the projective tests such as the Rorschach and
House-Tree-Person test. Those responsible for assessing risk should
evaluate the instruments in terms of predicting recidivism or, in specific cases, the behaviors of interest (e.g., sexual recidivism, domestic
assault, institutional misconduct). Assessment instruments originally
developed to predict psychological-emotional adjustment simply will
not do.
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GUIDELINE 3: USE ASSESSMENT INSTRUMENTS THAT
ARE DIRECTLY RELEVANT TO CRIMINAL BEHAVIOR
In corrections, there are numerous outcomes of importance. Correctional workers may be interested, for example, in whether the individual is likely to reoffend in the community, be a management problem in prison, suffer a psychotic breakdown, or attempt suicide. To my
knowledge, there is no single assessment instrument that can predict
all of these behaviors. On the other hand, I do not think that we need a
specific instrument for each criterion behavior that we are interested
in predicting.
There are two general classes of behaviors that are important to corrections: (a) rule violation (in a very general sense) and (b) psychological instability. Sometimes these behaviors are interrelated (e.g., paranoid delusions and assault on staff), but not always (e.g., severe
depression is unrelated to violent recidivism). For the most part, clinicians are trained to think that poor emotional and psychological health
will lead to deviant behavior. Consequently, recent clinical graduates
enter corrections believing that psychological maladjustment is a
strong correlate of criminal behavior. In addition, they believe alleviating anxiety and feelings of depression and increasing self-esteem
and so forth will help their clients follow a prosocial lifestyle. However, as I try to explain more fully under the next guideline, a focus on
psychological instability is not particularly helpful in reducing the
likelihood of criminal behavior. To make matters worse, the clinical
training received in many of our universities propagate the myth of
psychological disturbance as a correlate of criminal behavior by training psychologists in tests that measure psychological adjustment
(e.g., MMPI, Beck’s Depression Inventory, etc.).
Studies of the MMPI have not shown the instrument to be a particularly good predictor of rule-violating behaviors. Scale 4 or the old Pd
Scale, which was originally developed for the diagnosis of psychopathy, shows predictive correlation coefficients in the .16 to .21 range
(Gendreau, Little, & Goggin, 1996; Simourd, Bonta, Andrews, &
Hoge, 1991). These correlations were significant but did not exceed
the correlations found with the PCL-R or Gough’s Socialization Scale
(r = .29 to .39). Even when the MMPI was subjected to modifications
intended to make it suitable for offender populations, the results have
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not been impressive. For example, the Megargee MMPI typology
(Megargee & Bohn, 1979) did not predict recidivism as well as the
LSI-R risk-need instrument (Motiuk, Bonta, & Andrews, 1986).
The assessment of psychological instability for risk assessment is
largely irrelevant. Despite the empirical findings, assessment measures of psychological functioning remain extremely popular
(Boothby & Clements, 2000). Why this remains so is partly because of
the university training most psychologists receive. Very few universities in North America offer any specialty training in correctional or
forensic psychology.
Another reason for the wide use of tests assessing psychological
stability, I would suggest, is that psychological tests afford psychologists the semblance of a service no one else can provide. Correctional
staff and managers often have risk assessment as their primary concern. Convincing correctional managers that psychological tests can
improve risk assessment makes psychologists a useful human
resource. Unfortunately, the research suggests that the favorite tests of
psychologists are not very good compared to the offender risk scales
that can be completed by nonpsychologists with a few days of training. Psychologists should recognize the weakness of many psychological tests for the purpose of risk assessment. It may seem to the
reader that I am being excessively harsh on my psychologist colleagues. It is not my intention to minimize their value. I think that psychologists have made important contributions to the field, and these
are outlined elsewhere (Andrews & Bonta, 1998a; Bonta & Cormier,
1999). What I hope to do is encourage psychologists to reconsider
how they use some of their tests and make changes that are guided by
the research.
GUIDELINE 4: SELECT INSTRUMENTS
DERIVED FROM RELEVANT THEORY
Assessment in a correctional context can go a long way if the tools
of assessment are grounded in a theory of criminal behavior. Theories
of human behavior direct us to what should be assessed and guide us in
the interventions to change behavior. Without flooding the reader in
the details of the many theories developed to explain criminal behavior, we can consider three broad perspectives. They are (a) sociological-
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criminological explanations of crime, (b) psychopathological models,
and (c) social learning perspectives.
Sociological-criminological theories (e.g., anomie or strain theory,
conflict, labeling) place the root cause of crime in the broad social,
political, and economic structure of society. People engage in criminal
behavior because of frustrations arising from poverty, class conflict,
and power imbalances. These theories, however, are silent regarding
the assessment of offenders. An offender assessment instrument
derived from the sociological-criminological perspective would be
very easy to use and very short. For example, the following three-item
scale may suffice: (a) How much money do you make? (b) What is
your race? and (c) Do you belong to a socially disadvantaged group?
Some practitioners may welcome such a scale. Unfortunately for
these practitioners but fortunately for offenders and the field of corrections, the empirical evidence supporting sociological-criminological
theories of crime is weak (see Andrews & Bonta, 1998a, for a review).
Then we have the psychopathological perspectives of criminal
behavior. These are the explanations that attribute criminal behavior
to biological, emotional, cognitive, or psychological dysfunction.
Individuals engage in crime because they have too much testosterone,
or they are overly anxious and impulsive, intellectually deficient, egocentric and callous, or a bit of each. Furthermore, there is no shortage
of assessment measures. We have medical tests to assess biological
factors such as testosterone levels and the presence of an extra Y chromosome, if we choose. There are also many validated measures of
intelligence and other psychological and emotional traits.
Although empirically validated measures tapping biological and
psychological factors are abundant, the relevance of these variables to
predicting criminal behavior is tenuous. A number of meta-analyses
rank psychopathological variables in the low to mid range of predictive accuracy. Heredity comes in at r = .06 (for adoption studies;
Walters, 1992), r = .10 for intelligence (Cullen, Gendreau, Jarjoura, &
Wright, 1997), and r = .05 for general “personal distress” factors
(Gendreau et al., 1996). The relative importance of these variables
remains stable regardless of whether the criminal sample consists of
sex offenders (Hanson & Bussière, 1998) or mentally disordered
offenders (Bonta et al., 1998).
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Bonta et al.’s (1998) meta-analysis examined the relationship
between various psychological disturbances and recidivism. Serious
mental disorders (schizophrenia and manic depression) showed
hardly any association with violent recidivism (r = –.04 and r = .01,
respectively). Moreover, evidence of any mental disorder was negatively associated with general recidivism (r = –.19) and violent recidivism (r = –.10). Yet efforts to keep psychological disturbance factors
in the assessment of offender risk continue as exemplified in the HCR20 (Webster et al., 1997). The HCR-20 consists of 20 items covering
three areas (historical, clinical, and risk management). The Clinical
subscale items include, for example, mental illness symptoms, and the
Risk Management subscale has items such as stress. Research on the
predictive validity of HCR-20 is just now being reported, and it is
unclear what is the added value of the clinical factors to the prediction
of recidivism. In a study of civilly committed psychiatric patients, the
Clinical subscale demonstrated the lowest Area Under the Curve (.63)
of the three components in the prediction of violent recidivism
(Douglas et al., 1999).
The third general theoretical perspective encompasses the social
learning theories. The general personality and social learning model
forwarded by Andrews and Bonta (1998a) is an example of a social
learning theory that has empirical support and practical applications.
To briefly summarize the theory, criminal behavior is learned through
complex interactions between cognitive, emotional, personality, and
biological factors and environmental reward-cost contingencies.
Within the model, there are a number of factors or paths that lead to
criminal conduct, and some factors are more important than others.
Andrews and Bonta (1998a) described what they call “the Big
Four”—criminal history, antisocial personality, antisocial attitudes,
and social support for crime. Other, less important but nonetheless relevant variables in the model are indicators of prosocial convention
(e.g., employment and education), family relationships, and facilitators and inhibitors of antisocial and conventional behavior (e.g., substance abuse).
Evidence in support of the social learning perspectives is remarkably robust. For example, in a meta-analysis of the predictors of criminal behavior among general offenders, Gendreau et al. (1996) found
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the predictors ranked according to the theory outlined by Andrews and
Bonta (1998a). The best predictors of recidivism were the Big Four.
Meta-analytic reviews of the correlates of criminal behavior in other
offender samples also reported results consistent with a general personality and social-psychological perspective (see Bonta et al., 1998,
for a review with mentally disordered offenders, and Hanson &
Bussière, 1998, for a review with sex offenders). Treatment programs
that adhere to social learning principles are also more likely to reduce
recidivism than do nonbehavioral interventions (Andrews & Bonta,
1998a).
There are relatively few offender assessment instruments that have
ties to theories of crime and delinquency. Examples of the few that do
exist are the I-Level (Jesness, 1988) and Conceptual Level (CL) (Hunt
& Hardt, 1965) classification systems, the Psychopathy Checklist–
Revised (PCL-R) (Hare, 1990), and the Level of Service Inventory–
Revised (Andrews & Bonta, 1995). The I-Level and CL classification
systems, more properly, are based on theories of general emotional
and social development. In the I-Level system, individuals are seen as
going through seven stages of integration with the environment.
Delinquents fall into the second through fourth stages of unsocialized
aggressive, cultural conformist, and neurotic acting out (Sullivan,
Grant, & Grant, 1957). In the CL system, there are four stages of cognitive development, and delinquents are hypothesized to operate at the
first stage (egocentric thinking). Although the I-Level and CL classification systems are theory based, there has been little research on the
predictive validity of the two assessment approaches. Data that do
exist are limited to institutional adjustment rather than criminal recidivism (Van Voorhis, 1994).
The PCL-R and instruments that incorporate the PCL-R have
shown impressive predictive validity with respect to offender recidivism. Hare and his colleagues (Harpur & Hare, 1994; Hart, Hare, &
Forth, 1993) have made important contributions to our understanding
of psychopathy and antisocial personality. Essentially, Hare hypothesized that there are two essential characteristics to the psychopathic
personality: a peculiar affective and interpersonal style (i.e., egocentric, manipulative) and a socially deviant lifestyle. Beyond the
descriptive contributions, application of the PCL-R for the identifica-
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tion of psychopaths has facilitated laboratory work on biological, cognitive, and other psychological areas related to the construct.
Somewhat related to the PCL-R is the Violence Risk Appraisal
Guide (VRAG) (Harris et al., 1993). The VRAG was not specifically
derived from theory, but I mention it here because the PCL-R forms
part of the scale. The VRAG, therefore, deserves some recognition as
being influenced by theory. In addition to the PCL-R, the VRAG consists of 11 items, with nearly all of them historical in nature (e.g., age
at index offense, elementary scholastic maladjustment). Some recent
studies of the VRAG have shown large predictive validity estimates
(see Quinsey et al., 1998, for a review). More will be said about the
VRAG later.
The LSI-R is the most theoretically based offender assessment
instrument available today. The instrument consists of 10 subcomponents
that tap the areas outlined in the theoretical model outlined by
Andrews and Bonta (1998a). The Big Four are represented in the
subcomponent areas of criminal history, companions, emotional/
personal, and attitudes/orientation. In addition, a number of lesser but
important factors supported in theory and research are addressed in
the other subcomponents of the LSI-R (e.g., employment/education,
family/marital). The predictive validity of the LSI-R is well established, and reviews can be found elsewhere (Andrews & Bonta, 1995,
1998a; Gendreau et al., 1996; Gendreau, Goggin, & Smith, 2002 [this
issue]). The theoretically based LSI-R has two other important characteristics that make it attractive to staff responsible for managing
offenders: (a) comprehensiveness and (b) the assessment of criminogenic
needs. These are discussed under the next two guidelines.
GUIDELINE 5: SAMPLE MULTIPLE DOMAINS
Criminal behavior has many causes. There is no one factor that we
can point to and say that is what caused the criminal act. If we accept
the proposition of multiple determinism, and the evidence fully
expects that we do, then good offender assessment must attend to a
variety of factors or domains. In other words, comprehensive,
multidomain sampling of the factors associated with criminal conduct
should be the norm in offender risk assessment. Thus, we should be
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measuring criminal history, antisocial attitudes and values, antisocial
personality, social supports for crime, and other domains predictive of
criminal behavior. From a predictive accuracy perspective, Andrews,
Wormith, and Kiessling (1985) have demonstrated that increased
sampling of the domains related to criminal behavior improved
prediction.
Criminal history items are present in almost all offender risk scales.
For some scales, criminal history actually represents the majority of
items. Criminal history variables represent half of the items on the
HCR-20, 13 of 15 items on the Statistical Information on Recidivism
(used in Canada) (Nuffield, 1982), six of seven items on the Salient
Factor Score (used in the United States) (Hoffman, 1994), and four of
six items on the Offender Group Reconviction Scale (used in the
United Kingdom) (Copas & Marshall, 1998). However, criminal history is but one domain associated with criminal conduct. There are
other relevant domains, but they tend to be poorly distributed in many
scales. Offender assessment instruments, for the most part, have
“favorite” factors or domains to measure.
The PCL-R was designed to measure the domain of antisocial personality or, more specifically, psychopathy. However, criminal history
is also assessed. That is, more than one domain is measured. Similarly,
if we inspect the items that form other offender risk scales, such as the
HCR-20 and VRAG, then we can see that they all measure some of the
factors known to predict criminal behavior. None of these instruments, however, was developed with the explicit intention of sampling
a variety of domains.
The LSI-R consists of 10 subcomponents that are intended to sample the major domains associated with criminal behavior. As with the
other offender risk scales, criminal history is an important domain
(representing 10 of the 54 items). However, there are also subcomponents
devoted to the domains of antisocial support for crime, personality
characteristics conducive to criminal action, and procriminal attitudes. The remaining six subcomponents assess other lesser domains
(e.g., family/marital, substance abuse, etc.). Although the variety of
domains assessed has not necessarily led to significantly superior predictive accuracy compared to risk measures that only assess one or
two domains, the comprehensive feature of the LSI-R offers a signifi-
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cant advantage over the other scales for the management of offender
risk. These advantages are evident in the next section.
GUIDELINE 6: ASSESS CRIMINOGENIC NEED FACTORS
Criminogenic needs are aspects of a person or his or her situation
that, when changed, are associated with changes in criminal behavior
(Andrews, Bonta, & Hoge, 1990). Criminogenic needs are not simply
“any old needs”—they are dynamic risk factors. I underscore the
words dynamic risk to emphasize two important corollaries that derive
from the construct of dynamic risk. The first has to do with prediction
and the second with treatment.
With respect to prediction, the concept of dynamic risk draws attention to the fact that risk factors can be of two general types. They can
be static or dynamic. Static factors do not change or change only in
one direction (unfortunately, age is an example of a static factor). Furthermore, there is no reason to think that one type is superior to the
other when it comes to predicting recidivism. In fact, the comparison
of static and dynamic risk factors presented in Gendreau et al.’s (1996)
meta-analysis found little difference. The corrected mean effect size
for static variables was .11, and it was .13 for dynamic variables.
There are a few offender instruments that incorporate dynamic risk
factors along with static risk factors. They are referred to as risk-needs
scales. The two major examples from this class of instruments are the
LSI-R and the Wisconsin classification system. Once again, the riskneeds instruments predict as well as static risk scales. Gendreau et al.
(1996) found average effect sizes of .33 and .32 for the LSI-R and Wisconsin, respectively. The two static instruments in their meta-analysis,
the Salient Factor Score and the PCL-R, demonstrated slightly lower
effect sizes: .26 and .29, respectively. A more recent review of the predictive validities of the LSI-R and PCL-R found similar results
(Gendreau et al., 2002).
This discussion leads me to ask an important question. If risk
assessment instruments that consist of static and dynamic factors predict recidivism as well as instruments that consist solely of static factors, then why do we need static scales for the prediction of recidivism? Some may answer by saying that many static risk scales are
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brief and require minimal training to administer. Therefore, they are
highly efficient. However, some risk scales, such as the Screening Version of the LSI-R, can be both brief and dynamic (Andrews & Bonta,
1998b). More important, when we consider the risk management
advantages offered by assessment instruments that incorporate dynamic
risk factors, the arguments about efficiency pale in comparison.
The importance of the dynamic, risk-needs assessments for case
management is that they identify criminogenic needs that serve as targets for correctional treatment. Probation and parole officers, correctional officers, and treatment providers need to formulate how
criminogenic needs will be addressed in the case management plans
for offenders. The offender rehabilitation literature shows that reducing criminogenic needs is associated with reduction in recidivism
(Andrews & Bonta, 1998a; Andrews, Zinger, et al., 1990).
The importance of administering and readministering tests following treatment is obvious to psychologists and others who deliver treatment services. It is a way of measuring progress in treatment. Now that
we have reasonable knowledge about what constitutes a criminogenic
need, correctional workers can monitor the efficacy of their interventions. Effective interventions reduce criminogenic needs.
There are many measures of criminogenic needs. Some (i.e., the
risk-needs classification instruments) are broad and general and others are fairly specific (e.g., measures of substance abuse, anger and
hostility, antisocial attitudes). At the general level, the LSI-R is one of
the few risk-needs assessment instruments with evidence of dynamic
predictive validity (Andrews & Robinson, 1984; Motiuk, Bonta, &
Andrews, 1990; Raynor, Kynch, Roberts, & Merrington, 2000).
Changes in LSI-R scores on reassessment were associated with parallel changes in recidivism rates. Offenders who scored higher at
posttest showed an increase in recidivism, whereas offenders who
scored lower showed a decrease.
Numerous specific measures developed to assess criminogenic
needs have evidence for their predictive validity, but research on their
dynamic validity is almost nonexistent. For example, tests such as the
Addiction Severity Index (McClellan et al., 1985) and the Aggression
Questionnaire (Buss & Perry, 1992) predict relevant criteria (i.e., drug
abuse, aggressive behavior). However, to my knowledge, there are no
published studies showing that changes in scores on retest are associ-
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ated with changes in the outcome. One noteworthy exception to
domain-specific tests is the Criminal Sentiments Scale (CSS)
(Gendreau, Grant, Leipciger, & Collins, 1979), a measure of antisocial attitudes. The total score on the scale or scores on the subcomponents
have predicted recidivism among probationers (Andrews & Wormith,
1984) and violent inmates (Simourd & van de Ven, 1999). Evidence
for the dynamic validity of the CSS was provided in a study by
Andrews and Wormith (1984). One hundred and fifty-two probationers were tested on the CSS and retested 6 months later. Increased retest
scores were associated with increased recidivism, and decreased scores
were associated with lower recidivism 3 years following testing.
Theoretically and empirically, antisocial personality has a prominent role in the understanding of criminal behavior (Andrews &
Bonta, 1998a). Unfortunately, antisocial personality tends to be construed as a static, immutable personality disorder. Most professionals
would describe Hare’s (1998) psychopathy or the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 1994) antisocial personality disorder in this way. Antisocial personality, however, does not need to be viewed as such a stable,
intractable aspect of the person. Andrews and Bonta (in press) have
adopted a broader interpretation that defines antisocial personality as
a constellation of temperamental, personality, and behavioral traits,
many of which are dynamic (e.g., poor self-regulation and problemsolving skills, impulsiveness, risk taking, a callous disregard for others, and restless energy). When described this way, they are positing a
dynamic antisocial personality.
Empirical support for considering antisocial personality as a
dynamic personality constellation is found in a study by Simourd and
Hoge (2000). The PCL-R was administered to 321 inmates to identify
36 psychopaths (scores of 30 or more). The psychopaths were then
compared to the nonpsychopathic inmates on the LSI-R. The hypothesis was that psychopaths would have more criminogenic needs
(dynamic risk factors) than the nonpsychopaths. Of the nine dynamic
subcomponents of the LSI-R, the psychopaths scored significantly
higher than the nonpsychopaths on eight subcomponents (there were
no differences on the financial subcomponent). Thinking of psychopathy or antisocial personality in terms of risk-needs offers a more optimistic agenda for managing offenders with this personality constella-
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tion. At the very least, information on treatment targeting is provided
thereby giving hope for change.
Even if treatment is not a concern to the correctional agency,
dynamic risk-needs instruments can help staff monitor changes in
offenders and their situations that influence their risk for criminal
behavior. For example, probation and parole officers must assess
whether changes in the offender’s behavior are serious enough to warrant a suspension and return to prison. Unfortunately, the assessment
of change is left largely in the hands of clinical and professional judgment. The periodic reassessment of risk-needs can go a long way
toward correcting this situation.
Sex offenders under community supervision represent a situation
where the monitoring of risk-needs is of paramount importance. Failure to recognize a shift toward higher risk of sexually reoffending can
have very serious consequences. Currently, there are no empirically
based risk-needs assessment instruments to assist community supervisors in monitoring change in sex offenders. Recently, however, there
have been a few reports of dynamic risk assessment instruments that
may be used for the supervision of sex offenders. A cross-sectional
study of sex offenders by Simourd and Malcolm (1998) found LSI-R
scores significantly correlated with deviant sexual arousal and the
denial and minimization of sex-offending behavior. In a retrospective
study, Hanson and Harris (2000b) identified a number of dynamic factors that were associated with sexual recidivism. These factors were
then organized to form the Sex Offender Need Assessment Rating
(SONAR) (Hanson & Harris, 2000a). A modified version of SONAR
is now being validated in a longitudinal study (R. K. Hanson, personal
communication, January 9, 2001).
In summary, what the correctional field really needs are tools to
help staff manage risk. To manage risk effectively, knowledge of
dynamic risk factors is critical. Consequently, static risk scales have
limited utility. Offender assessment scales that measure dynamic risk
factors provide comparable predictive value, thereby making static
scales somewhat redundant. Furthermore, I would suggest an
overreliance on static risk instruments may even be counterproductive. Static risk scales, by portraying individuals as “unchangeable,”
run the risk of diverting offenders away from treatment. Broad-based,
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social learning perspectives of criminal conduct do not rely on static
factors to explain crime, and neither should our assessment instruments.
GUIDELINE 7: LIMIT GENERAL PERSONALITY AND COGNITIVE
TESTS TO THE ASSESSMENT OF RESPONSIVITY
Boothby and Clements (2000) found that 87% of correctional psychologists used the MMPI in their work. We do not know why the
MMPI was administered—it is hoped not for assessments of risk for
recidivism. Tests like the MMPI and other personality and cognitive
measures can, however, play a role in offender assessment. They may
be useful with respect to responsivity considerations. The responsivity
principle of offender classification states that the style and mode of
treatment must be matched to the cognitive, personality, and sociocultural characteristics of the individual (Andrews, Bonta, & Hoge,
1990; Bonta, 1995).
Classification instruments such as the I-Level and CM are examples of assessment tools that tap responsivity considerations. For
example, in the I-Level system, offenders are classified along stages
of interpersonal maturity, and interventions are matched to the stage
of development. Thus, offenders in the I-3 stage (manipulator) need to
be dealt with in a way that recognizes and guards against their manipulative interpersonal style. Offenders at different I levels may pose
equal risks for reoffending and have similar criminogenic needs, but
the way the needs and risk level are addressed differ depending on
their interpersonal maturity level.
Although my example treated the assessment of responsivity separately from risk and needs, some measures of responsivity can assess
risk and criminogenic needs. The I-Level, CM, and MMPI demonstrate poor predictive validity with respect to recidivism and therefore
contribute minimally to the assessment of risk and criminogenic needs
(for a review of the research, see Andrews & Bonta, 1998a). However,
the assessment of antisocial personality is an example where information relevant to risk, needs, and responsivity are given. The PCL-R
research can be used to describe what I mean. The PCL-R has shown
good predictive validity for general and violent recidivism; that is,
assessments with the PCL-R yield information on risk. As explained
previously, it has not been used to formulate treatment planning (i.e.,
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need and responsivity). Yet the PCL-R can provide information for
this purpose. Examination of the individual items in the PCL-R yields
three groupings. There are static risk factors (e.g., early behavior
problems, criminal versatility), dynamic criminogenic needs (e.g.,
pathological lying, poor behavioral controls), and responsivity factors
(e.g., grandiose sense of self-worth, shallow affect). Thus, the construct of antisocial personality or psychopathy assesses not only risk
and needs but also responsivity factors.
Treatment programs for offenders labeled antisocial personality or
psychopathic have recognized the need to deal with these offenders
differently from other offenders. With antisocial personality disordered offenders, the reinforcement contingencies within group sessions must be tightly controlled, extra attention must be paid to motivational issues, and a concerted effort must be made to have them
assume greater responsibility for their behavior (Hare, 1998; Wong,
2000). Assessments of mental disorders (e.g., schizophrenia, manic
depression, etc.) also are important for responsivity consideration.
The static and dynamic risk factors among mentally disordered
offenders are highly similar to the general offender population (Bonta
et al., 1998) but how programming is delivered to offenders with a
mental disorder will certainly influence the success of treatment.
Finally, assessment of responsivity is not limited to the personalitycognitive attributes of offenders. Broader social and cultural factors
also play a role. We do not need tests to assess gender, race, and ethnicity, but program delivery must attend to these factors. Although the
risk assessment research across sociocultural groups is not particularly well developed, it appears that the risk factors for criminal behavior are similar across gender (Simourd & Andrews, 1994) and race and
ethnicity (Bonta, LaPrairie, & Wallace-Capretta, 1997; Lowenkamp
& Latessa, 2000). Once again, for treatment to have an impact, the
style of service must be tailored to the social, cultural, and ethnic characteristics of the offenders.
GUIDELINE 8: USE DIFFERENT METHODS TO ASSESS RISK AND NEEDS
Any test has error associated with it, and therefore, no single test
predicts perfectly. The sources of error vary (e.g., poor conceptualization of the construct being measured, lack of motivation on the part of
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the client, etc.). One important source of error is the format used in
assessment. The predictive validity of assessments can improve when
different methods are combined. When different methods are used,
the weakness of one assessment instrument can be compensated by
the strength of another test. There are four common methods used in
offender assessment: (a) paper-and-pencil, (b) interview-based, (c)
behavioral, and (d) file extraction procedures. Each method has
known weaknesses, and many are well known. Paper-and-pencil
questionnaires yield poor results if the client fails to understand the
questions, assessments conducted by interview are open to the personal biases of the examiner, behavioral assessments may be overly
specific and contrived, and file extraction methods are limited by the
quality of the information in the files.
In the area of corrections, all four methods of assessing offender
risk have been used. Paper-and-pencil measures are used in general
offender classification (e.g., Jesness, 1988, I-Level; Megargee &
Bohn, 1979, Megargee’s MMPI-based system) and for more specialized assessments such as the assessment of antisocial attitudes (e.g.,
Pride in Delinquency [PID] scale) (Shields & Whitehall, 1991;
Simourd & van de Ven, 1999) and the risk for violence (e.g., SelfAppraisal Questionnaire) (Loza & Loza-Fanous, 2000, 2001).
Among the interview-based measures, well-known examples are the
LSI-R, PCL-R, and HCR-20. Behavioral measures include the Adult
Internal Management System (Quay, 1984). Finally, measures using
file extraction procedures are represented by the VRAG and, for the
risk of sexual recidivism, STATIC-99 (Hanson & Thornton, 1999).
Many of the tests illustrated in the previous paragraph sample different domains. Some measure only one domain (e.g., PID: antisocial
attitudes). Others sample two or three domains (e.g., PCL-R: criminal
history, personality, attitudes), whereas some instruments, such as the
LSI-R, sample up to 10 different domains relevant to criminal conduct. These measures also use different methods. The question that I
raise in this section is what would happen if we use different methods
to assess the same domain. For example, if we were interested in
assessing antisocial attitudes, would we gain predictive accuracy if we
were able to assess the construct using multiple methods?
An affirmative answer to this question was provided in a study by
Andrews and his colleagues (Andrews, 1985; Andrews et al., 1985). I
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present only their results with respect to the assessment of antisocial
attitudes to illustrate the improvement achieved by introducing multimethod assessment. Probationers were administered a paper-andpencil measure of antisocial attitudes (CSS) and followed up 3 years
later. Scores on the CSS correlated .46 with recidivism. When an
interview-based assessment of antisocial attitudes was added (the
antisocial attitude subcomponent of the LSI-R), the multiple correlation increased to .63.
GUIDELINE 9: EXERCISE PROFESSIONAL RESPONSIBILITY
Guidelines 9 and 10 are brief but serve as reminders to use tests
responsibly. The results from offender risk assessments have profound effects not only on the freedom of offenders but also on the
safety of staff and community. Those who administer offender risk
scales must ensure that they are well trained in their administration
and knowledgeable of the current issues surrounding offender assessment. Although few staff members will ever find themselves in court
to defend their assessment, they should be prepared to do so. This
requires each staff member to be ready to explain how a test is used,
the research on its predictive validity, and the theory supporting the
test. These expectancies are endorsed by professional associations
that have responsibilities for the appropriate administration of tests
(e.g., American Psychological Association, 1974). If a test administrator cannot explain the proper use of a test and the empirical support
for it—and one does not have to be an expert in psychometrics—then
continued administration of the test is professionally questionable.
GUIDELINE 10: BE NICE
At this point, the reader may think that this last guideline was an
afterthought, something added to form a “top 10” list. I did not begin
writing this article with a top 10 list in mind but, I must admit that I was
a bit delighted when I wrote Guideline 8 and knew that I still wanted to
write a couple more things. My last guideline reflects my own personal values, and they probably do not agree with everyone’s views.
I think that the application of offender risk assessments should
adhere to the principle of the least restrictive alternative. The principle
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states that the punishment of offenders should use the least intrusive
measures and only to the extent needed to manage their behavior
(Rubin, 1975). Thus, imprisonment should be reserved for the most
recalcitrant of offenders, intermediate sanctions for the nonviolent,
and perhaps minimal or no intervention for the lowest risk offenders.
Obviously, our criminal justice system does not appear to follow very
closely the least restrictive principle. With nearly 1.3 million people in
U.S. prisons, it is hard to imagine that community alternatives are not
appropriate for any of them.
Risk assessment is a double-edged sword. It can be used to justify
the application of severe sanctions or to moderate extreme penalties. It
is easy to “sell” a risk instrument when the instrument claims to identify the dangerous and justifies the imprisonment of those individuals.
However, the identification of the violent recidivist is not infallible.
We are not at the point where we can achieve a level of prediction that
is free from error. And considering the high levels of incarceration, I
sometimes wonder whether risk instruments serve to identify the
highly dangerous a priori or simply serve to justify decisions already
made.
Using risk instruments to identify lower risk offenders who can be
placed in a noncustodial setting is a tougher sell (albeit the identification of low-risk offenders is also not perfect). I learned this the hard
way. In the early LSI-R research, I tried to convince various jurisdictions that we were overusing incarceration as a sanction and that we
could use the instrument to identify inmates for community placement. That is, there were many offenders who did not need to be in
prison for long periods of time. Many of my arguments fell on deaf
ears. Larry Motiuk, who worked with me on a number of these projects, joked that maybe the name should be changed from Level of Service Inventory to “Locked Securely Inside.” I am glad that we followed the least restrictive custody principle.
SUMMARY
What I tried to do in this article is outline what I see as the general
issues in offender risk assessment and encourage the reader to reflect
on his or her own use of risk instruments. The past couple of decades
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have seen significant improvements to assessing offender risk. Today,
actuarial approaches are widely accepted as preferable to clinical
assessments, the predictive accuracy of instruments has been steadily
improving, “criminogenic need” has slipped into the assessment lexicon, and the use of classification instruments to guide case management and treatment interventions is becoming more common.
Despite the advances, we still have a long way to go. The use of the
best and most current offender assessment instruments is not widespread. One only needs to be reminded of the survey by Boothby and
Clements (2000). Educating and encouraging psychologists and other
professional staff about the latest research on offender assessment
must continue. I am pleased that this journal and special editor have
brought together a collection of articles that promote the development
of a more informed use of offender assessment instruments.
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