Assessing Violent Recidivism in Sexual Offenders
Hollida Wakefield and Ralph Underwager*
In: ipt, volume 10, 1998
ABSTRACT: Forensic and clinical psychologists have long been asked to make
predictions about violence, despite the fact that, in the past, such predictions
have been notoriously inaccurate. Several states now have sexual predator
laws which require predictions to be made concerning the likelihood of
recidivism. Since the U.S. Supreme Court in Kansas v. Hendricks
(1997) upheld Kansas's sexual predator laws, such requests are likely to
increase in the future. Fortunately, there is now ongoing empirical
research which has improved psychologists' ability to predict violence in high
risk groups. Several schemes for predict violence are in the process of
research and development.
The Sexual Predator Laws
In the 1980s, several states began to pass sexual predator statutes that required sexual offenders judged likely to reoffend to be civilly committed until they were judged to be no longer at risk. The constitutionality of these statutes was challenged in Kansas vs. Hendricks and on June 23, 1997 the U.S. Supreme court handed down a 5 to 4 decision confirming the constitutionality of the statute. Currently there are sexual predator statutes in several states, including Arizona, California, Illinois, Kansas, Minnesota, North Dakota, New Jersey, Washington, and Wisconsin, and Delaware and Missouri have prefiled bills along the same lines. Other states are likely to pass such laws; 45 states and territories filed briefs supporting the Kansas law (Gordon, 1998) and Michigan and New York have developed similar legislation (Doren, 1998). The difficulties in predicting dangerousness have not stopped courts from using such predictions in sexual offender commitment proceedings and it is well-established that there is no constitutional barrier to using such predictions in legal proceedings, including those that result in loss of liberty (Janus & Meehl, 1997).
The sexual predator laws are applied to those who are about to be released
from prison following the completion of their sentences. To commit an
individual, the state must prove that (1) the person has committed sexual
offenses in the past, (2) the person currently suffers from a mental disorder,
and (3) the person is likely to commit a sex offense in the future. The
civil commitment follows a jury trial in which the jury finds that the person is
likely to engage in predatory acts of sexual violence in the future.
Common to the various sexual predator laws is the requirement that mental health
professionals assess the degree of sexual offense risk in thousands of
individuals (Doren, 1998).
Predicting Recidivism and Base Rates
The prediction of future violence is difficult, complex, and controversial, and psychologists and psychiatrists do not have a good track record in making accurate predictions. But since John Monahan's (1981) influential book on predicting violent behavior, there has been a great deal of research in this area resulting in improvement in the ability of clinicians and researchers to make these predictions (Monahan, 1996; Webster, Harris, Rice, Cormier, & Quinsey, 1994).
The fundamental problem is that in the general population, violent behavior is a low frequency event. Attempting to predict events in a population with a low antecedent probability leads to an unacceptable level of false positives. If the base rate for violence in a given population is very low, then the most accurate prediction is always to predict that a given individual will not be violent. Any assessments of individual cases will produce less accurate results over the long run.
When statistical methods are applied to a population with a higher frequency of violent behavior, i.e., the prison population or those with a history of violence, more reasonable predictions can be made. Therefore, recent research on high frequency violence populations indicates that the accuracy of predicting future violent behavior can be improved over chance by the use of actuarial methods.
This includes sexual violence since the research on predicting violent recidivism in general is relevant to predicting sex offender recidivism. In the procedures for assessing violent recidivism, sex offenses have been included in the category of violent recidivism. The sexual offense does not have to involve actual physical violence. Webster, Douglas, Eaves and Hart (1997) state that "all sexual assaults should be considered violent behaviour" (p. 25). Boer, Wilson, Gauthier, and Hart (1997) define sexual violence as "actual, attempted, or threatened sexual contact with a person who is nonconsenting or unable to give consent" (p. 328). Webster et al. (1994) include all sex offenses in their sample. Hanson and Bussière (1996), however, conclude on the basis of their meta-analysis that sexual recidivism is best predicted by a different set of factors, which includes sexual deviance.
The base rate for sexual recidivism for certain offenders is high enough that an actuarial prediction method can improve the accuracy of prediction when the definition of recidivism is in keeping with the sexual offender commitment laws. The recidivism rate, however, differs among various studies. Hanson and Bussière (1998) report that only a minority (13.4%) of their total sample of 23,393 subjects from their meta-analysis committed a new offense within the average 4- to 5-year follow-up period. Even with studies with thorough record searches and follow-up periods of 15 to 20 years, the recidivism rate never exceeded 40%. A universal finding in the literature is that incest offenders have the lowest rates of reoffending.
In contrast, Doren (1998), in a review of the research, reports that the true recidivism base rate over 25 years for extrafamilial sexual abusers is 52% and for rapists is 39%. Doren, who is involved with the sexual predator program at Mendota Mental Health Institute in Wisconsin, uses the recidivism rates from Prentky, Lee, Knight, and Cerce (1997). This is an extremely high risk sample. The Prentky, et al. sample consisted of 251 men who were committed to the Massachusetts Treatment Center for Sexually Dangerous Persons (MTC). Persons who were charged after being released from MTC and persons who were residents at MTC but were previously discharged, reoffended and were recommitted were included in the sample. Also, a charge, not a conviction, was used as the index of reoffense.
In addition, the figures of 39% and 52% are estimates from the survival
analysis; the percentage of new offenses at the end of the study period (25
years) was 26% for rapists and 32% for child molesters. Doren maintains
that the survival analysis provides a more accurate approximation of actual
Clinical Versus Actuarial Predictions
There are two general approaches to risk assessment — actuarial and clinical. In the clinical approach, the clinician makes a judgment about the person based on his or her training, experience, and clinical impressions of the person being assessed. Actuarial methods are the quantified, impartial, and systematic use of factors from the file or history of the person. The essential requirement is that the data are quantified, and statistical procedures based on the laws of probability are employed. The prediction thus rests on base rates, known probabilities, specific historical factors, and past conduct. A clinical interview alone cannot be used to make an accurate prediction.
There is no more solidly established fact in the science of psychology than
the superiority of statistical, actuarial procedures over clinical judgments in
making decisions (Dawes, Faust, & Meehl, 1989, 1993). More than 50
years of research shows that in making decisions the information to be relied
upon first is the statistical and actuarial data that are available.
Clinical judgments, based only on personal opinions unsupported by empirical,
quantified data, must be regarded with considerable caution and relied upon over
statistical information only when there is a credible, compelling, and cogent
basis for doing so. The risk prediction schemes that have resulted in
improvements over chance in the ability of psychologists to make accurate
predictions about sexual recidivism have a strong actuarial base.
Risk Factors Associated With Recidivism
Risk factors associated with violent behavior, including sexual offender recidivism, are both static and dynamic (for a good discussion of this see Proulx, Pellerin, Paradis, McKibben, Aubut, & Ouimet, 1997). Static factors that predict recidivism, such as age, offense history, childhood family factors, cannot be changed. Dynamic factors, such as criminal attitudes and progress in treatment, are potentially changeable. Research on an actuarial and more accurate system for predicting the probability that an individual will engage in violent behavior in the future mainly uses static factors, although dynamic factors can modify the prediction.
In general, the factors most strongly related to violent and sexual recidivism include having the characteristics of psychopathy as defined by a high PCL-R score (i.e. Hare, 1991, 1996, in press; Hart & Hare, in press; Rice, 1997), a history of criminal behavior, and being young. Rice and Harris (1997) report that the combination of psychopathy, measured by the PCL-R, and sexual deviancy, based on phallometric test results, resulted in the highest recidivism rate in their sample of sex offenders.
Hanson and Bussière (1996) report on the factors that predicted sexual offender recidivism in their meta-analysis. This is an important study in that, at the time of their study, there had been 87 different articles containing 61 different sets of data that dealt with factors that are related to sexual offender recidivism. Not all of the studies report the same factors. Therefore, the meta-analysis allows the factors from the different studies to be considered as a whole.
As noted above, Hanson and Bussière (1996) found that, overall, the rate of sexual offense recidivism in the 61 studies they performed their meta-analysis on was low. When recidivism was defined as any offense (not just sexual reoffending), the overall recidivism rate was higher, 36.3%. They also report that sexual offenders classified as "mentally disordered sexual offenders" under the sexual psychopath laws of several states were only slightly more likely to reoffend than other sexual offender groups.
Their review also suggested that sexual recidivism is best predicted by a different set of factors than those that predict general or nonsexual violent recidivism. Although general criminological variables, such as age and prior offenses, showed some relationship with sexual recidivism, they report that the strongest predictors were variables related to sexual deviance. No single factor was sufficiently related to recidivism to justify its use in isolation, but the authors presented predictor factors that had a statistically significant relationship to recidivism. They do not present an organized scheme for considering and weighing the factors and making a prediction.
Risk factors for sexual recidivism identified by Hanson and Bussière's (1996, 1998) meta-analysis are shown in Table 1 (listed in the order of the strength of the correlations between the risk factor and recidivism).
Some factors that clinicians have assumed to be related to sexual offense
recidivism, such as denial of the sex offense, empathy for victims, a history of
being sexually abused as a child, and general psychological problems were not
found to predict sexual offense recidivism (Hanson & Bussière 1996, 1998).
Violence Prediction Methods
Hanson and Bussière's (1996, 1998) meta-analysis is not a violence
prediction method. They do not present an organized scheme for considering
and weighing the factors and making an actuarial prediction. A number of
the factors correlated with recidivism also overlap with each other and are
therefore likely highly intercorrelated. This means that simply counting
the factors likely produces an overinterpretation because more than one factor
will be counted on the same basis for an individual. For example, a person
with a diagnosis of an antisocial personality disorder will also be counted for
any personality disorder. This inflates the number of factors
counted. It is therefore a mistake to do a risk assessment by simply
counting the number of risk factors that the person may have.
Psychopathy Checklist-Revised (PCL-R)
Psychopathy is defined by Hare (1991):
Although psychopathy is sometimes confused with antisocial personality disorder, it must be differentiated from this diagnosis and the relationship between the PCL-R diagnosis of psychopathy and the DSM diagnosis of APD is an asymmetric one, at least in forensic populations. On average, about 90% of criminal psychopaths meet the DSM criteria for APD, but only 20% to 30% of inmates with APD also meet the PCL-R criteria for psychopathy (Hare, 1991, Hart & Hare, in press).
The PCL-R has been validated on both male prison inmates and on male forensic psychiatric patients. It shows very high levels of accurate prediction of violence and recidivism and adds significantly to the best possible predictive accuracy that is based solely on criminal history (Annon, undated; Hare, 1996, 1998; Harris, Rice, & Quinsey, 1993; Hart & Hare, in press; Rice, 1997; Salekin, Rogers, & Sewell, 1996; Webster et al., 1994). The PCL:SV, a research and screening version that takes less time to complete can be used as a screen for psychopathy in forensic populations. It can also be used with civil populations (Hare, 1996, 1998).
The PCL-R requires collateral and file information in order to rate the individual — ratings cannot be made on the basis of interviews alone. Although the interview is important for obtaining information about the individual's interpersonal style, as well as gathering historical information, if an interview is impossible, valid ratings can be obtained on the basis of collateral information alone if there is sufficient high-quality information.
The authors recommend that evaluators attend a three-day training workshop
before using the PCL-R/PCL:SV (information on workshops can be obtained from
Robert Hare at (604) 822-3611 or on the webpage http://www.hare.org/).
Since the PCL-R is used in several of the risk assessment schemes discussed
below it is important for professionals involved in sexual predator assessment
to be proficient in its use.
Rapid Risk Assessment for Sexual Offense Recidivism (RRASOR)
Hanson (1997) recently developed the Rapid Risk Assessment for Sexual Offense Recidivism (RRASOR). The RRASOR, which is based on data from seven studies, contains four items that are easily scored from administrative records: prior sexual offenses, age less than 25, extrafamilial victims, and male victims. The scale showed moderate predictive accuracy (r = .27) and Hanson believes it is useful as a screening instrument in settings that require routine assessments of sexual offender recidivism risk (see Table 2).
Violence Prediction Scheme
Webster, Harris, Rice, Cormier, and Quinsey (1994) developed the Violence Prediction Scheme for assessing dangerousness in high risk men on the basis of a sample of men committed to the maximum security division of a mental health center in Canada. The scheme consists of two parts: an actuarial component based on the Violence Risk Appraisal Guide (VRAG) and a 10-item clinical scheme called the ASSESS-LIST (antecedent history, self-presentation, social and psychosocial adjustment, expectations and plans, symptoms, supervision, life factors, institutional management, sexual adjustment, and treatment progress). Their sample included men committed for sexual offenses.
Unlike the Hanson and Bussière (1996) analysis, the VRAG lists the risk factors associated with recidivism and how to assess them, the weights assigned to each, and provides a table of the relationship between the VRAG scores and the probability of violent recidivism for 7-year and 10-year follow-up intervals. There are difficulties in generalizing from this, since the population made up of referrals in other jurisdictions is not identical to the population used by Webster et al. (1994). Nevertheless, the VRAG represents an advance over merely counting risk variables.
The variables included in the VRAG are the Hare PCL-R (psychopathy) score, elementary school maladjustment, age at index offense, diagnosis of personality disorder, separation from parents when the person was under age 16, failure on prior conditional release, criminal history of nonviolent offenses, marital status of never married, diagnosis of schizophrenia, victim injury in index offense, history of alcohol abuse, and male victim in index offense. Weights are assigned to these variables. All of them are related positively except age, diagnosis of schizophrenia, and victim injury, which are negatively related.
After assessing and weighing the actuarial factors, the clinical factors (ASSESS-LIST) are considered. These variables are those that they and others have found to be related to violent behavior but have not reached the point where they are added to the actuarial formula. Some of the factors discussed by Hanson and Bussière (1996) above are included here. Webster et al. (1994) recommend using these clinical factors in the overall prediction, but stress using caution in altering the actuarial judgment.
In their 1998 book, however, the authors changed their advice to include these clinical factors. They now state that there is "an extremely high probability that clinically adjusted VRAG predictions are less accurate than unadjusted scores" (Quinsey, Harris, Rice & Cormier, p. 163).
The VRAG shows excellent predictive accuracy (r = .47) for general violent
recidivism (Webster et al., 1994) and less for sexual recidivism (r = .20) (Rice
& Harris, 1997). The authors recommend using it both for general and
for sexual recidivism (Rice & Harris, 1997; Marnie Rice, personal
communication, 8/20/97 & 8/22/97). Some reviewers, however (i.e.,
Borum, 1996; Robert Hare, personal communication), have urged caution in
generalizing their findings to other populations. But Monahan (1995)
states that "the violence prediction scheme is so far superior to anything
previously available that not to seriously consider its use, at least on an
experimental basis, in other jurisdictions would be a difficult choice to
justify" (p. 447).
Sex Offender Risk Appraisal Guide (SORAG)
Although the Webster et al. (1994) have recommended using the VRAG for
assessing risk of recidivism in sex offenders, they have recently developed a
variation specifically intended for sex offenders, the Sex Offender Risk
Appraisal Guide (SORAG). This is described in Quinsey, et al., 1998.
According to Marnie Rice (personal communication, 3/16/98), the SORAG only does
a little better than the VRAG with sex offenders and has not been validated on
as many subjects. She states that there is not yet any big advantage to
using it, other than it was designed specifically for sex offenders and the
scoring of some of the variables fits with most people's intuition better than
with the VRAG.
The HCR-20 (Webster, Douglas, Eaves, & Hart, 1997) arose out of an attempt to provide a systematic way for clinicians to perform risk assessments in a manageable procedure. (HCR-20 refers to the fact that there are 20 items in three categories, Historical, Clinical, and Risk Management.) The original HCR-20 was published in 1995 and the authors describe the current version as a "work in progress" (p. vi.). Borum (1996) notes that the HCR-20's primary value is as a checklist to prompt the examiner to cover or consider the major areas of inquiry. Unlike the VRAG, the HCR-20 can be best characterized as a structured guide to clinical assessment.
A similar instrument designed for use with sex offenders is the Sexual
Violence Recidivism-20 (SVR-20) (Boer, Wilson, Gauthier, & Hart,
1997). The authors state that the SRV-R risk factors are not intended to
be used as an actuarial scale and they cannot recommend a decision-making
algorithm. Instead, "evaluators should consider the SVR-20 and any
other case-specific factors deemed important, and should integrate them in an
unstructured or 'clinical' manner" (p. 336).
Minnesota Sex Offender Screening Tool (MnSOST)
The MnSOST (Minnesota Department of Corrections, 1997), which was developed
in response to the sexual predator laws in Minnesota, is designed to help
identify the most violent offenders and those offenders most likely to reoffend.
The evaluator rates the person on 21 items that are given different scores and
then ranks the inmate's dangerousness on a scale of 1 to 10. The MnSOST is
used to screen inmates for possible referral for civil commitment as well as for
determination of the person's community notification risk level. The
validity scale sample consisted of 256 Minnesota sex offenders released from
prison between 1988 and 1993. Sex offenders arrested for a subsequent sex
offense had higher mean scores than those who were not. The authors state
that with a cut off point of 47 and above, 41 of the 66 offenders (62%) who had
these scores were arrested for a subsequent sex offense. Since the base
rate for rearrest for the entire sample was 41%, the authors state that the
MnSOST provides a 50% improvement over chance in prediction of rearrest.
There can be no question but that making the most accurate decision possible serves the welfare and benefit of all involved. The aim to protect the society from harm is advanced by accurate decisions both in precluding further criminal acts and in avoiding the social and financial costs of unnecessary imprisonments. The scientific evidence suggesting that a statistical, actuarial approach improves accuracy of decisions made should be taken seriously. Given the current climate of anxiety and fear about crime and the trend toward more punitive and draconian punishment, including the factor of statistical, actuarial methods in the decision process may serve both to increase the accuracy of decisions and avoid potential well intentioned but possibly erroneous rush to judgment. Given the high cost of imprisonment and the ever more limited funds available, more accurate decision making will benefit all by increasing the effectiveness of detention and deterrence at the lowest possible cost.
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