The actuarial method is the gold standard for risk assessment in child welfare, juvenile justice, and criminal justice. It produces risk classifications that are highly predictive and that may be robust to sampling error. This article reports a revalidation study of the Arizona Risk/Needs Assessment instrument, an actuarial instrument for juvenile offenders. Results of this study (N = 29,711) show that the actuarial model provided stable predictive validity estimates for recidivism up to 7 years after its initial validation and that predictive validity did not vary appreciably for males, females, White offenders, Latino youth, African American youth, and Native American youth. These findings lend further support to the actuarial approach to risk assessment and clarify the literature about risk assessment across gender and race/ethnicity.
Andrews, D.W., Bonta, J., & Hoge, R.D. (1990). Classification for effective rehabilitation: Rediscovering psychology. Criminal Justice and Behavior, 17, 19-52.
2.
Baird, C. (1984). Classification of juveniles in corrections: A model systems approach. Madison, WI: National Council on Crime and Delinquency.
3.
Burgess, E. W. (Ed.). (1928). Factors determining success or failure on parole. Springfield : Illinois State Board of Parole.
4.
Burgess, E.W. (1936). Protecting the public by parole and by parole prediction. Journal of Criminal Law and Criminology, 27, 491-502.
5.
Dean, C.W., & Duggan, T.J. (1968). Problems in parole prediction: A historical analysis . Social Problems, 15, 450-459.
6.
Funk, S.J. (1999). Risk assessment for juveniles on probation: A focus on gender. Criminal Justice and Behavior, 26, 44-68.
7.
Gambrill, E., & Shlonsky, A. (2000). Risk assessment in context. Children and Youth Services Review, 22, 813-837.
8.
Glaser, D. (1987). Classification for risk. Crime and Justice, 9, 249-291.
9.
Gottfredson, D. M., & Tonry, M. (Eds.). (1987). Prediction and classification: Criminal justice decision making. Chicago: University of Chicago Press.
10.
Griffin, P., & Bozynski, M. (2003). National overviews: State juvenile justice profiles . Retrieved November 5, 2003, from http://www.ncjj.org/stateprofiles/
11.
Hall, S.M., & Brannick, M.T. (2002). Comparison of two random-effects methods of meta-analysis. Journal of Applied Psychology, 87, 377-389.
12.
Howell, J.C. (1995). Guide for implementing the comprehensive strategy for serious, violent, and chronic juvenile offenders. Washington, DC: Office of Juvenile Justice and Delinquency Prevention.
Huedo-Medina, T.B., Sanchez-Meca, J., Marin-Martinez, F., & Botella, J. (2006). Assessing heterogeneity in meta-analysis: Q statistic or I-2 index? Psychological Methods, 11, 193-206.
15.
Hunter, J.E., & Schmidt, F.L. (2000). Fixed effects vs. random effects meta-analysis models: Implications for cumulative research knowledge. International Journal of Selection and Research, 8, 275-292.
16.
Ilacqua, G.E., Coulson, G.E., Lombardo, D., & Nutbrown, V. (1999). Predictive validity of the Young Offender Level of Service Inventory for criminal recidivism of male and female young offenders . Psychological Reports, 84, 1214-1218.
Jung, S., & Rawana, E.P. (1999). Risk and need assessment of juvenile offenders . Criminal Justice and Behavior, 26, 69-89.
19.
Krysik, J., & LeCroy, C.W. (2002). The empirical validation of an instrument to predict risk of recidivism among juvenile offenders. Research on Social Work Practice, 12, 71-81.
20.
LeCroy, C.W., Krysik, J., & Palumbo, D. (1998). Empirical validation of the Arizona Risk/Needs Instrument and assessment process. Tucson, AZ: LeCroy & Milligan Associates.
21.
Lipsey, M.W., & Wilson, D.B. (1998). Effective intervention for serious juvenile offenders . In R. Loeber & D. P. Farrington (Eds.), Serious & violent juvenile offenders (pp. 313-345). Thousand Oaks, CA: Sage.
22.
Meehl, P.E. (1954). Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. Minneapolis: University of Minnesota Press.
23.
Meehl, P.E. (1986). Causes and effects of my disturbing little book . Journal of Personality Assessment, 50, 370-375.
24.
Monachesi, E.D. (1950). American studies in the prediction of recidivism . Journal of Criminal Law and Criminology, 41, 269-289.
25.
Monahan, J., Steadman, H.J., Silver, E., Appelbaum, P.S., Robbins, P.C., & Mulvey, E.P. (2001). Rethinking risk assessment: The MacArthur study of mental disorder and violence. Oxford, UK: Oxford University Press.
Rice, M.E., & Harris, G.T. (1995). Violent recidivism: Assessing predictive validity . Journal of Consulting and Clinical Psychology, 63, 737-748.
28.
Rice, M.E., & Harris, G.T. (2005). Comparing effect sizes in follow-up studies: ROC area, Cohen's d, and r. Law and Human Behavior, 29, 615-620.
29.
Rosenthal, R. (1991). Meta-analytic procedures for social research (2nd ed.). Newbury Park, CA: Sage.
30.
Rosenthal, R., & DiMatteo, M.R. (2001). Meta-analysis: Recent developments in quantitative methods for literature reviews. Annual Review of Psychology, 52, 59-82.
31.
Schwalbe, C.S. (2007). A meta analysis of juvenile justice risk assessment predictive validity. Law and Human Behavior, 31, 449-462.
32.
Schwalbe, C.S., Fraser, M.W., & Day, S.H. (2007). Predictive validity of the Joint Risk Matrix with juvenile offenders: A focus on gender and race/ethnicity. Criminal Justice and Behavior, 34, 348-361.
33.
Schwalbe, C.S., Fraser, M.W., Day, S.H., & Arnold, E.M. (2004). North Carolina Assessment of Risk (NCAR): Reliability and predictive validity with juvenile offenders. Journal of Offender Rehabilitation, 40, 1-22.
34.
Schwalbe, C.S., Fraser, M.W., Day, S.H., & Cooley, V. (2006). Classifying juvenile offenders according to risk of recidivism: Predictive validity, race/ethnicity, and gender. Criminal Justice and Behavior, 33, 305-324.
35.
Sharkey, J.D., Furlong, M.J., Jimerson, S.R., & O'Brien, K.M. (2003). Evaluating the utility of a risk assessment to predict recidivism among male and female adolescents. Education and Treatment of Children, 26, 467-494.
36.
Shlonsky, A., & Wagner, D. (2005). The next step: Integrating actuarial risk assessment and clinical judgment into an evidence-based practice framework in CPS case management. Child and Youth Service Review, 27, 409-427.
37.
Silver, E., Smith, W.R., & Banks, S. (2000). Constructing actuarial devices for predicting recidivism: A comparison of methods. Criminal Justice and Behavior, 27, 733-764.
38.
Swets, J.A. (1996). Signal detection theory and ROC analysis in psychology and diagnostics: Collected papers. Mahwah, NJ: Lawrence Erlbaum.
39.
Towberman, D.B. (1992). A national survey of juvenile risk assessment . Family & Juvenile Court Journal, 43, 61-67.
