Predicting Parolee Risk of Recidivism --Challenge of Finding Instruments with Sufficient Predictive Power

1. Predicting Parolee Risk of Recidivism--Challenge of Finding Instruments with Sufficient Predictive Power Association for Criminal Justice Research (California) 66th. Semi-annual Meeting, October 11- 12, 2007 Sheldon Zhang, SDSU David Farabee, UCLA Robert Roberts, CSU San Marcos

2. The Need for Reentry Risk Assessment • Five millions of adults on probation and parole nationwide. • High rates of incarceration in the U.S. means high volumes of prisoner reentry. High rates of parole failures lead to additional imprisonments. • Risk/needs assessments can best allocate resources and afford appropriate supervision plans. These assessments can guide sentencing, institutional placement, treatment plans, parole supervision intensity, and the restrictiveness of conditions for community reentry. • Risk/needs assessment has again gained traction in recent years in correctional agencies in several states. • A recent study by the Girls Study Group identified some 300 risk/needs assessment tools of various kinds for youth offenders alone. Most lack evidence of sufficient validation. Many studies report reliability but not as much on validity* *. Margaret A. Zahn. 2006. Issues in Assessing Risk with Delinquent Girls. Girls Study Group. Crime, Violence, and Justice Program, RTI. Available at: http://girlsstudygroup.rti.org/docs/2006_NIJ_Conference_Risk_Assessment.pdf.

3. Need to Test and Validate Risk Instruments • Development of risk instruments is often based on specific correctional populations, and does not transplant easily. • LSI-R model that was developed in Canada and found to be predictive in Canadian correctional populations in several studies. • Studies in Washington and Pennsylvania show that many factors used in the LSI-R scale were not predictive of re-offending (Austin 2004). • In one study in Pennsylvania, only eight of the 54 LSI-R items were found to be associated with recidivism. Significant inter-rater reliability problems were also found (Austin et al. 2003). • A risk assessment instrument needs to be tested in its intended population. • Instruments developed and tested with general populations or unintended populations may lead to over-classification (an unreasonable number of false positives in either direction).

4. Predictive Accuracy of LSI-R* • In 1999, the Washington State Department of Corrections began using LSI-R, as part of the offender risk classification system. • A 2003 Institute study found that this instrument is not a strong predictor of felony and violent felony recidivism for Washington State offenders. • A later analysis again found that LSI-R as a whole predicts felony sex recidivism with weak accuracy (AUC=.65). Five items on the LSI-R can be combined to predict felony sex recidivism with moderate accuracy. *. Robert Barnoski, 2006. Sex offender sentencing in Washington state: Predicting recidivism based on the LSI-R. Available at: http://www.wsipp.wa.gov/rptfiles/06-02-1201.pdf.

5. Some Examples • Level of Service Inventory-Revised (LSI-R). Comprised of 54 static and dynamic items across ten sub-scales (O’Keefe and Wensus, 2001); developed in the late 1970s in Canada through a collaboration of probation officers, correctional managers, practitioners and researchers (AUC .65 in a Washington state validation study). • Washington State Department of Corrections Static Risk Instruction (based on LSI-R) (AUC .74) (http://www.wsipp.wa.gov/rptfiles/07-03-1201.pdf ). • Virginia’s Risk Assessment Instrument, developed by the Virginia Criminal Sentencing Commission for sentencing and diversion purposes (http://www.ncsconline.org/WC/Publications/Res_Senten_RiskAssessPub.pdf ): • Higher “risk scores” on the instrument have been associated with a greater likelihood of recidivism • Diversion through risk assessment has produced positive net benefits for the state • No AUC was computed.

6. Ways to Assess Risk Assessment Tools • Correlation analysis • Multivariate regression • Stepwise logistic regression

7. Area Under the ROC Curve The best measure of predictive accuracy between risk assessment and recidivism is the Area Under the Receiver Operating Characteristic Curve. AUC measures discrimination--the ability of the instrument to correctly classify different levels of risk in anticipation of recidivism. Instrumentation: suppose we have a group of parolees who were already correctly classified (those who failed parole and those who didn’t). You randomly select one who failed parole and one who didn’t and developed a profile of risk factors. The one with a higher level of risk should be the one who failed. AUC calculates the percentage of randomly drawn pairs for which the risk classification is correct. AUC varies between .50 (pure chance) and 1.00 (prefect prediction). AUC less than .60 is considered weak, .70 moderate, .80 strong.* *T.G. Tape, 2003, Interpreting Diagnostic Tests, The Area Under the ROC Curve, Omaha: University of Nebraska Medical Center, see: http://gim.unmc.edu/dxtests/roc3.htm. Source: http://gim.unmc.edu/dxtests/roc3.htm

8. The Challenge of Finding Instruments with Sufficient Predictive Power—A Canadian Comparison Study • Assessment of five actuarial instruments and one guided clinical instrument designed to assess risk for recidivism were compared on 215 sex offenders released from prison for an average of 4.5 years. These five actuarial instruments are objectively scored and provide probabilistic estimates of risk based on the empirical relationships between their combination of items and the outcome of interest. • Violence Risk Appraisal Guide (VRAG) (Harris, Rice, & Quinsey, 1993), • Sex Offender Risk Appraisal Guide (SORAG) (Quinsey, Harris, Rice, & Cormier, 1998) • Rapid Risk Assessment of Sexual Offense Recidivism (RRASOR) (Hanson, 1997) • Static-99 (Hanson & Thornton, 1999) • Minnesota Sex Offender Screening Tool–Revised (MnSOST-R) (Epperson, Kaul, & Hesselton, 1998). • Psychopathy Checklist–Revised (PCL-R) (Hare, 1991)

9. AUC of the Receiver Operating Characteristic for the Six Risk Assessment Instruments No one instrument was found to be superior in predicting recidivism outcomes. Barbaree et al. 2001. Evaluating the predictive accuracy of six risk assessment instruments for adult sex offenders. Criminal Justice and Behavior 28(4): 490-521.

10. Relative Predictive Accuracy of the RRASOR, SACJ-Min and Static-99 Combined Sample (n = 1,208) Rapists Child Molesters (n = 363) (n = 799) ROC Area 95% C.I. r 95% C.I. ROC area ROC area Sexual recidivism RRASOR .68 .65-.72 .28 .23-.33 .68 .69 SACJ-Min .67 .63-.71 .23 .18-.28 .69 .68 Static-99 .71 .68-.74 .33 .28-.38 .71 .72 Any violent Recidivism RRASOR .64 .60-.67 .22 .16-.27 .64 .66 SACJ-Min .64 .61-.68 .22 .16-.27 .62 .66 Static-99 .69 .66-.72 .32 .27-.37 .69 .71 R. Karl Hanson and David Thornton. 2002. Static 99: Improving Actuarial Risk Assessments for Sex Offenders, 1999-02. Available at: http://ww2.ps-sp.gc.ca/publications/corrections/199902_e.pdf.

11. COMPAS • COMPAS (Correctional Offender Management and Profiling Alternative Sanctions) is a computerized database and analysis system for criminal justice practitioners to make decisions regarding the placement, supervision and case-management of offenders in community and secure settings. • The system includes several modules: • risk/needs assessment, • criminal justice agency decision tracking, • treatment and intervention tracking, • outcome monitoring, • agency integrity and programming implementation monitoring.

12. COMPAS—Risk and Needs Assessment • CDCR adopted the risk/needs components. • Current study evaluates the risk assessment component, which includes four dimensions: • recidivism • violence • failure to appear • community failure • Offenders are classified into three categories: high, medium, and low risk.

13. Previous validation study by the instrument developers (Northpointe) found encouraging psychometric properties and concurrent validity, based on retrospective data. • Our study attempts to address COMPAS’ predictive validity. • Observation period=365 days

14. Demographics • Show word file.

15. Status of COMPAS Subjects at One Year

16. COMPAS Recidivism Scale (Outcome: Returned To Custody in 365 Days of Parole)

17. COMPAS Community Non-Compliance Scale (Outcome: Returned To Custody in 365 Days of Parole)

18. Failure-To-Appear Risk Scale Score Decile (Outcome: Technical Parole Violation in 365 Days of Parole)

19. Statistical Analysis • AUC for COMPAS for Recidivism = .67 • AUC for COMPAS for Non-Technical Parole Violation = .61 • Adding other static variables in existing CDCR warehouse data can improve COMPAS Recidivism subscale to .72.

20. Odds-Ratios from Logistic Regression of Return to Custody within One Year on COMPAS Risk Measures and Parolee Characteristics (Males only, N = 457)

21. Next Step • Search for static variables to increase AUC. • Wait for larger sample size for validation. • Explore possibilities to conduct a head-to-head comparison between parole agents’ judgments and COMPAS assessment. • Example: In 1998, ADJC collaborated with NCCD to develop the Arizona Risk/Needs Instrument. • Subsequent validation found the assessment method was less accurate at predicting risk than probation officer’s judgments.