Partially Specified Actuarial Tables and the Poor Performance of Static-99R

1. Partially Specified Actuarial Tables and the Poor Performance of Static-99R Richard Wollert Ph.D. Jacqueline Waggoner Ed.D. Washington State Vancouver University of Portland rwwollert@aol.com waggoner@up.edu http://richardwollert.com wordpress.up.edu/waggoner 360.737.7712 503.943.8012 American Psychology-Law Society March 2013 Portland, OR

2. Actuarial Instruments for Sex Offender Risk Assessment • Contain “risk items” correlated with sexual recidivism. • Each risk item is subdivided into categories. • An offender is assigned to only 1 category per risk item. American Psychology-Law Society March 2013 Portland, OR

3. Actuarial Manual • Sets forth criteria for assigning offenders to item categories. • Contains coding rules that weight each category. • Some categories scored as zero, some as 1 or more, a few as – 1 or less. American Psychology-Law Society March 2013 Portland, OR

4. Actuarial Manual • An offender is assigned to a “risk group” per his score. • Some groups include a range of scores. We call them “bins.” American Psychology-Law Society March 2013 Portland, OR

5. One-Way Model (Once Called “Partial Specification” but Dropped as a Misnomer) • Tries to capture the effects of risk factors on recidivism with a single number. • First generation actuarials were one-way models. • The 10-item Static-99 is an example. • Offenders got one point for “current age less than 25.” No points if older. American Psychology-Law Society March 2013 Portland, OR

6. One-way Actuarial Table for Static-99 Score Bins and Point Scores (from Hanson & Thornton, 2000, p. 129). American Psychology-Law Society March 2013 Portland, OR

7. The “Age Invariance Effect”(Hirschi & Gottfredson, 1983) • Sexual recidivism declines with age throughout life (Hanson, 2002). • The decline is linear. • The effect applies to all risk bins (Wollert, 2006; Hanson, 2006). • Static-99 combined bin-wise rates for all ages. • This masked the fact that different age groups have different recidivism rates. American Psychology-Law Society March 2013 Portland, OR

8. Static-99 Underestimated Young Offender Rates (-%) and Overestimated Old Offender Rates (+%) Even With Optimum (Unweighted) Scaling L American Psychology-Law Society March 2013 Portland, OR

9. The MATS-1 (Wollert et al., 2010) Took Into Account the Linearity of Age Invariance and Addressed the Estimation Errors of Static-99 MATS-1 = “Multisample Age-Stratified Table of Sexual Recidivism Rates.” Removes age item from Static-99, so it has 9 “non-age predictor” (NAP) items. Recidivism focus is on an offender’s age and NAP score (able to capture interactions). Also called a “two-way” model. American Psychology-Law Society March 2013 Portland, OR

10. MATS-1 Recidivism Rates American Psychology-Law Society March 2013 Portland, OR

11. Static-99R Is A One-Way Model Designed To Account For The Age Effect • Described in Helmus et al., 2012. • Age-weighting was used. • 18-34 group: One point added. • 40-59 group: One point subtracted. • 60-70+ group: Three points subtracted. American Psychology-Law Society March 2013 Portland, OR

12. Static-99R Performed Poorly • Construction sample ROC = .708. • Validation sample ROC = .720. • Static-99 validation sample ROC = .713. • Recidivism rate for the Static-99R high bin < 27%. American Psychology-Law Society March 2013 Portland, OR

13. How Age-Weighting Undermined Static-99R’s Performance: Part 1 of a 3 Part Story • 243 young offenders were moved to the highest risk bin from lower Static-99 bins because they received an extra point. • This is “upscale dilution.” Less dangerous offenders are mixed with more dangerous offenders = high bins have lower rates (Waggoner et al., 2008). American Psychology-Law Society March 2013 Portland, OR

14. How Static-99R’s Performance Was Undermined by Age-Weighting: Part 2. • 230 old offenders were taken out of the high bin and moved to lower bins because they received negative points. • This is “downscale enrichment.” More dangerous offenders are mixed with less dangerous offenders = low bins have higher rates. American Psychology-Law Society March 2013 Portland, OR

15. How Static-99R’s Performance Was Undermined by Age-Weighting: Part 3. • The numbers of recidivists and nonrecidivists in each bin were about the same for Static-99 and Static-99R when offender data were pooled across age groups. • Itis impossible to obtain accuracy differences using ROC tests when the binwise distributions of recidivists and nonrecidivists for two tests are about the same. American Psychology-Law Society March 2013 Portland, OR

16. The Number of Recidivists and Nonrecidivists In Each Static-99 and Static-99R Bin Were Similar American Psychology-Law Society March 2013 Portland, OR

17. Static-99R Bins Underestimate Recidivism for Young Offenders and Overestimate It for Old Offenders. American Psychology-Law Society March 2013 Portland, OR

18. Discussion • Age-weighting did not enhance Static-99R. • Like Static-99, it underestimates young offender rates and overestimates old offender rates. American Psychology-Law Society March 2013 Portland, OR

19. A Solution to Age-Weighting Problems: Convert Static-99R to a 2-Way Model • Take all the age points out of Static-99R. • Stratify Static-99R NAP bins by age in one table. • Use external data and frequency or Bayesian math to construct another table like the first. • Assign the cells in Table 1 to bins on the basis of the cell-wise recidivism rates in Table 2. • e.g., cells with very large rates in Table 2 make up Table 1’s “high” bin category, etc. American Psychology-Law Society March 2013 Portland, OR

20. References • Hanson, R. K. (2002). Recidivism and age. Journal of Interpersonal Violence,17, 1046-1062. • Hanson, R. K. (2006). Does Static-99 predict recidivism among older sexual offenders? Sexual Abuse: A Journal of Research and Treatment, 18, 343-355. • Hanson, R. K. & Thornton, D. (2000). Improving risk assessments for sex offenders: A comparison of three actuarial scales. Law and Human Behavior, 24, 119-136. • Helmus, L., Thornton, D., Hanson, R. K., & Babchishin, K. M. (2012). Improving the predictive accuracy of the Static-99 and Static-2002 with older sex offenders: Revised age weights. Sexual Abuse: A Journal of Research and Treatment, 24(1), 64-101. DOI: 10.1177/1079063211409951. American Psychology-Law Society March 2013 Portland, OR

21. References • Hirschi, T. & Gottfredson, M. (1983). Age and the explanation of crime. American Journal of Sociology, 89, 552-584. • Waggoner, J., Wollert, R., & Cramer, E. (2008). A respecification of Hanson’s updated Static-99 experience table that controls for the effects of age on sexual recidivism among young offenders. Law, Probability and Risk, 7, 305-312. • Wollert, R. (2006). Low base rates limit expert certainty when current actuarial tests are used to identify sexually violent predators: An application of Bayes’s Theorem. Psychology, Public Policy, and Law, 12, 56-85. • Wollert, R. (2007, August). Validation of a Bayesian Method for Assessing Sexual Recidivism Risk. Presented in San Francisco at the 2007 APA conference. http://www.richardwollert.com American Psychology-Law Society March 2013 Portland, OR

22. References Wollert, R., Cramer, E., Waggoner, J., Skelton, A., & Vess, J. (2010). Recent research (N=9,305) underscores the importance of using age-stratified actuarial tables in sex offender risk assessments. Sexual Abuse: A Journal of Research and Treatment, 22, 471-490. DOI: 10.1177/1079063210384633. Acknowledgements The authors are indebted to Brian Abbott, David Cooke, Ted Donaldson, Elliot Cramer, and Diane Lytton for reading and commenting on previous versions of this presentation. American Psychology-Law Society March 2013 Portland, OR