Risk assessment for juvenile justice: A meta-analysis

Law and Human Behavior

Volumn 31, Issue 5, 2007, Pages 449-462

  Schwalbe, Craig S ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Delinquency; Juvenile court; Juvenile justice; Meta analysis; Risk assessment

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; CORRELATION COEFFICIENT; CRIMINAL JUSTICE; HUMAN; JUSTICE; META ANALYSIS; PREDICTIVE VALIDITY; RISK ASSESSMENT;

ADOLESCENT; FEMALE; HUMANS; JUVENILE DELINQUENCY; MALE; RECURRENCE; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; UNITED STATES;

EID: 34548550435     PISSN: 01477307     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10979-006-9071-7     Document Type: Article

References (60)

1. *Ashford, J. B., & LeCroy, C. W. (1988). Predicting recidivism: An evaluatin of the Wisconsin Juvenile Probation and Aftercare Risk Instrument. Criminal Justice and Behavior, 15, 141-151.
2. *Ashford, J. B., & LeCroy, C. W. (1990). Juvenile recidivism: A comparison of three prediction instruments. Adolescence, 25, 441-450.
3. *Baker, K., Jones, S., Roberts, C., & Merrington, S. (2003). The evaluation of the validity and reliability of the Youth Justice Board's assessment for young offenders: Findings from the first two years of ASSET. Oxford: Centre for Criminological Research, University of Oxford.
4. Barbaree, H. E., Seto, M. C., Langton, C. M., & Peacock, E. J. (2001). Evaluating the predictive accuracy of six risk assessment instruments for adult sex offenders. Criminal Justice and Behavior, 28, 490-521.
5. Bonta, J. (1996). Risk-needs: Assessment and treatment. In A. T. Harland (Ed.), Choosing correctional options that work: Defining the demand and evaluating the supply (pp. 18-32). Thousand Oaks, CA: Sage.
6. *Barnoski, R. (2004). Assessing risk for re-offense: Validating the Washington State Juvenile Court Assessment. Olympia, WA: Washington State Institute for Public Policy.
7. Bonta, J., Law, M., & Hanson, K. (1998). The prediction of criminal and violent recidivism among mentally disordered offenders: A meta-analysis. Psychological Bulletin, 123, 123-142.
8. Burgess, E. W. (Ed.). (1928). Factors determining success or failure on parole. Springfield: Illinois State Board of Parole.
9. *Catchpole, R. E. H., & Gretton, H. M. (2003). The predictive validity of risk assessment with violent young offenders: A 1-year examination of criminal outcome. Criminal Justice and Behavior, 30, 688-708.
10. *Corrado, R. R., Vincent, G. M., Hart, S. D., & Cohen, I. M. (2004). Predictive validity of the psychopathy checklist: Youth version for general and violent recidivism. Behavioral Sciences & the Law, 22, 5-22.
11. Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical vs. actuarial judgment. Science, 243, 1668-1674.
12. DeVellis, R. F. (2003). Scale development: Theory and applications (2nd edn.). Newbury Park: Sage.
13. Downie, N. M., & Heath, R. W. (1983). Basic statistical methods (5th edn.). New York: Harper & Row.
14. Ferguson, J. L. (2002). Putting the "what works" research into practice: An organizational perspective. Criminal Justice and Behavior, 29, 472-492.
15. *Flores, A. W., Travis, L. F., & Latessa, E. J. (2004). Case classification for juvenile corrections: An assessment of the Youth Level of Service/Case Management Inventory (YLS/CMI), final report. Washington, DC: National Institute of Justice.
16. Fraser, M. W. (Ed.). (2004). Risk and resilience in childhood: An ecological perspective (2nd edn.). Washington, DC: NASW Press.
17. Gendreau, P., Goggin, C., & Smith, P. (2002). Is the PCL-R really the "unparralleled" measure of offender risk?: A lesson in knowledge cumulation. Criminal Justice and Behavior, 29, 397-426.
18. Gendreau, P., Little, T., & Goggin, C. (1996). A meta-analysis of the predictors of adult offender recidivism: What works. Criminology, 34, 557-607.
19. Gretton, H. M., McBride, M., O'Shaughnessy, R., & Kumka, G. (2001). Psychopathy and recidivism in adolescent sex offenders. Criminal Justice and Behavior, 28, 427-449.
20. Griffin, P., & Bozynski, M. (2003). National overviews: State juvenile justice profiles. Retrieved November 5, 2003, from http://www.ncjj.org/ stateprofiles/
21. Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical-statistical controversy. Psychology, Public Policy, and Law, 2, 293-323.
22. Hall, S. M., & Brannick, M. T. (2002). Comparison of two random-effects methods of meta-analysis. Journal of Applied Psychology, 87, 377-389.
23. Hodges, K., & Kim, C. (2000). Psychometric study of the child and adolescent functional assessment scale: Prediction of contact with the law and poor school attendence. Journal of Abnormal Child Psychology, 28, 287-297.
24. Hodges, K., & Wong, M. M. (1996). Psychometric characteristics of a multidimensional measure to assess impairment: The child and adolescent functional assessment scale. Journal of child and Family Studies, 5, 445-467.
25. Hoge, R. D. (2002). Standardized instruments for assessing risk and need in youthful offenders. Criminal Justice and Behavior, 29, 380-396.
26. *Hoge, R. D., & Andrews, D. A. (2001). The youth level of service /case management inventory (YLS /CMI): Intake manual and item scoring key. Carleton University.
27. 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.
28. Howell, J. C. (2003). Preventing & reducing juvenile delinquency: A comprehensive framework. Thousand Oaks, CA: Sage.
29. 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(2), 193-206.
30. 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.
31. Hunter, J. E., & Schmidt, F. (2004). Methods of meta-analysis (2nd edn.). Thousand Oaks, CA: Sage.
32. *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.
33. *Johnson, K., Wagner, D., & Matthews, T. (2002). Missouri juvenile risk assessment re-validation report. Madison, WI: National Council on Crime and Delinquency.
34. Jones, P. R., Harris, P. W., Fader, J., & Grubstein, L. (2001). Identifying chronic juvenile offenders. Justice Quarterly, 18, 479-507.
35. *Jung, S., & Rawana, E. P. (1999). Risk and need assessment of juvenile offenders. Criminal Justice and Behavior, 26, 69-89.
36. *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.
37. *LeCroy, C. W., Krysik, J., & Palumbo, D. (1998). Empirical validation of the Arizona risk/needs instrument and assessment process. Tucson, AZ: LeCroy & Milligan Associates, Inc.
38. Lipsey, M. W. (2003). Those confounded moderators in meta-analysis: Good, bad, and ugly. The Annals of the American Academy of Political and Social Science, 587, 69-81.
39. *NCCD. (2000). Alameda County placement risk assessment validation: Draft of final report. Oakland, CA: Self.
40. *Quist, R. M., & Matshuzi, D. G. M. (2000). The child and adolescent functional assessment scale (CAFAS): A dynamic predictor of juvenile recidivism. Adolescence, 35, 181-192.
41. Rice, M. E., & Harris, G. T. (1995). Violent recidivism: Assessing predictive validity. Journal of Consulting and Clinical Psychology, 63, 737-748.
42. Rice, M. E., & Harris, G. T. (2005). Comoparing effect sizes in follow-up studies: ROC area, Cohen's d, and r. Law and Human Behavior, 29, 615-620.
43. *Risler, E. A., Sutphen, R., & Shields, J. (2000). Preliminary validation of the juvenile first offender risk assessment index. Research on Social Work Practice, 10, 111-126.
44. Rosenthal, R. (1991). Meta-analytic procedures for social research (2nd edn.). Newbury Park: Sage Publications.
45. Rosenthal, R., & DiMatteo, M. R. (2001). Meta-analysis: Recent developments in quantitative methods for literature reviews. Annual Review of Psychology, 52, 59-82.
46. *Schmidt, F., Hoge, R. D., & Gomez, L. (2005). Reliability and validity analyses of the youth level of service/case management inventory. Criminal Justice and Behavior, 32, 329-344.
47. *Schwalbe, C. S., Fraser, M. W., & Day, S. H. (in-press). Predictive validity of the Joint Risk Matrix with juvenile offenders: A focus on gender and race/ethnicity. Criminal Justice and Behavior.
48. *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.
49. *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.
50. *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.
51. *Smith, W. R., & Aloisi, M. F. (1999). Prediction of recidivism among "second timers" in the juvenile justice system: Efficiency in screening chronic offenders. American Journal of Criminal Justice, 23, 201-222.
52. Steel, P. D., & Kammeyer-Mueller, J. D. (2002). Comparing meta-analytic moderator estimation techniques under realistic conditions. Journal of Applied Psychology, 87, 96-111.
53. Swets, J. A. (1996). Signal detection theory and ROC analysis in psychology and diagnostics: Collected papers. Mahwah, NJ: Lawrence Erlbaum Associates.
54. *Thompson, A. P., & Pope, Z. (2005). Assessing juvenile offenders: Preliminary data for the Australian adaptation of the youth level of service/case management inventory (Hoge & Andrews, 1995). Australian Psychologist, 40, 207-214.
55. Towberman, D. B. (1992). A national survey of juvenile risk assessment. Family & Juvenile Court Journal, 43, 61-67.
56. *Turner, S., & Fain, T. (2003). Validation of the Los Angeles County Probation Department's risk and needs assessment instruments. Washington, DC: Department of Justice.
57. *Upperton, R. A., & Thompson, A. P. (2005). Predicting recidivism: A risk assessment inventory vs juvenile justice officers. Paper presented at the Australian Psychological Society, Melbourne.
58. *Wagner, D., Wiebush, R., & Lunning, S. (2000). Validation of the Indiana DOC juvenile division risk assessment instrument. Madison, WI: National Council on Crime and Delinquency.
59. Walters, G. D. (2003). Predicting criminal justice outcomes with the Psychopathy Checklist and Lifestyle Criminality Screening Form: A meta-analytic comparison. Behavioral Sciences & the Law, 21, 89-102.
60. *Wiebush, R., Wagner, D., & Ehrlich, J. (1999). Development of an empirically-based risk assessment instrument: For the Virginia Department of Juvenile Justice final report. Madison, WI: National Council on Crime and Delinquency.