Saturday, July 7, 2007

What is the "Static 99" test?

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The management of sex offenders within the criminal justice system can be substantially influenced by the offender's perceived risk for recidivism. Those sex offenders deemed high risk may be subject to substantial restrictions, such as post-sentence detention, indeterminate sentences, and long-term community supervision. Conversely, sex offenders deemed to be low risk may be placed on probation and, if incarcerated, be considered for early release.

Although many decisions require risk assessments, the procedures used for making such assessments often have limited validity. In general, the average predictive accuracy of professional judgement to predict sex offence recidivism is only slightly better than chance (average r = .10, Hanson & Bussière, 1998). Some have even argued that the accuracy of prediction is sufficiently low that it threatens the very basis of risk-based legal sanctions for sex offenders (Janus & Meehl, 1997).

Recent research, however, has the potential of substantially improving the accuracy of recidivism risk assessments for sex offenders. Hanson and Bussière's (1998) meta-analytic review identified a number of risk factors that were reliably associated with sex offence recidivism. Most of these factors were static, historical variables related to sexual deviance (e.g., prior sex offences, stranger victims) and general criminality (e.g., prior non-sex offences, antisocial personality disorder). Several different actuarial risk instruments have also been developed to predict recidivism among sexual offenders (e.g., Sex Offender Risk Appraisal Guide [SORAG], Quinsey, Harris, Rice & Cormier, 1998; Minnesota Sex Offender Screening Tool – Revised [MnSOST-R], Epperson, Kaul & Hesselton, 1998); Rapid Risk Assessment for Sex Offence Recidivism [RRASOR], Hanson, 1997; Thornton’s Structured Anchored Clinical Judgement [SACJ], Grubin, 1998). These actuarial scales not only specify the items to consider, but also provide explicit direction as to the relative importance of each item. The items in the scales are similar, although the scales vary as to the relative weight accorded to the general factors of sexual deviance versus antisociality.

The SORAG (Quinsey et al., 1998) is a variation of the Violence Risk Appraisal Guide (VRAG; Quinsey et al., 1998) for sexual offenders. Like the VRAG, the SORAG was designed to assess any violent recidivism, not just sexual recidivism. It contains 15 items addressing early childhood behaviour problems, alcohol problems, sexual and nonsexual criminal history, age, marital status, and personality disorders (with a large weight on psychopathy). The MnSOST-R was developed to predict sexual recidivism among rapists and extrafamilial child molesters. The MnSOST-R includes 16 items addressing sexual and non-sexual criminal history, the victims’ age and relationship to the offender, substance abuse, unstable employment, age, and treatment history (Epperson et al., 1998). Both the RRASOR (Hanson, 1997) and SACJ (Grubin, 1998) were intended to be relatively brief screening instruments for predicting sexual offence recidivism.

The purpose of the present study was to compare the predictive accuracy of two of these actuarial schemes: the RRASOR (Hanson, 1997) and the SACJ (see Grubin, 1998). Although rarely used in North American, the SACJ is routinely used in Her Majesty’s Prison Service (England and Wales) and in many police departments in the UK. The SACJ contains items related to sexual deviance, but also places considerable weight on non-sexual criminal history. The RRASOR, in contrast, almost exclusively targets factors related to sexual deviance. The RRASOR is widely used in Canada and the U.S., being the most common risk assessment tool used in post-sentence detention procedures (Doren, 1999). Given the different emphasis of the RRASOR and SACJ, one goal of the current study was to examined whether a simple combination of these two scales could improve upon the predictive accuracy of either original scale.

Rapid Risk Assessment for Sex Offence Recidivism (RRASOR; Hanson, 1997)
The aim of the RRASOR was to predict sex offence recidivism using a small number of easily scored variables. The initial pool of seven items were those that correlated at least .11 with sex offence recidivism in Hanson and Bussière's (1998) meta-analysis and were commonly recorded: prior sex offences, any prior non-sex offences, any male victims, any stranger victims, any unrelated victims, never married, and age less than 25 years. In order to identify the most efficient combination of these items, the correlations between these predictor variables were calculated in seven different data sets (total sample of 2,592), and then averaged using standard meta-analytic techniques (Hedges & Olkin, 1985). Following a suggestion by Becker (1996), the averaged correlation matrix was then subjected to step-wise regression to identify the best predictor variables.

Of the original seven variables, four substantially contributed to the regression equation (beta greater than .09): prior sex offences, any unrelated victims, any male victims and age less than 25 (see Table I). The scale resulting from the simple combination of these four variables was then tested on an entirely new sample (HM Prison). Overall, the scale showed comparable predictive accuracy in both the development and validation samples (average r = .27; average ROC area = .71).

Structured Anchored Clinical Judgement (SACJ; Grubin, 1998).
The SACJ aims to predict sexual and violent recidivism using a stage approach, with each stage incorporating different types of information. The first stage considers the offender's official convictions: specifically, any current sex offences, any prior sex offences, any current non-sexual violent offences, any prior non-sexual violent offences, and four or more prior sentencing occasions (see Table 1). If offenders have four or more of the initial factors, they are automatically considered high risk. If two or three factors are present, offenders are considered medium risk, and zero or one factors indicate low initial risk.

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