Sage Journals: Discover world-class research

Abstract

Risk assessments such as the Youth Level of Service/Case Management Inventory (YLS/CMI) that predict delinquency outcomes based on proximal risk factors may benefit from an incorporation of distal risk factors in their prediction models. This study utilized a juvenile probationer sample and block group SES data in exploring the differential predictive validity of the YLS/CMI with youth of similar person-centered risk levels from different criminogenic neighborhood types. The study entailed an exploratory factor analysis of block group socioeconomic variables, which were used in a cluster analysis to create criminogenic neighborhood typology system. Hierarchical logistic regression was used to analyze the relationship among recidivism (Level 1), risk score (Level 1), neighborhood SES factors (Level 2), and neighborhood types (Level 2). Significant interactions were found across levels among variables, suggesting the risk—recidivism relationship was moderated by neighborhood socioeconomic ecology. Implications for practice and policy are discussed.

Keywords

delinquency risk assessment neighborhood research community psychology

Get full access to this article

View all access options for this article.

References

Anderson, A. ( 2002). Individual and contextual influences on delinquency: The role of the single-parent family. Journal of Criminal Justice, 30, 575-587.

Andrews, D.A. , & Bonta, J. ( 1995). The Level of Service Inventory-Revised. Toronto, Ontario, Canada: Multi-Health Systems.

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.

Chung, H. , & Steinberg, L. ( 2006). Neighborhood, parenting, and peer influences on antisocial behavior among serious juvenile offenders. Developmental Psychology , 42, 319-331.

Cottle, C.C. , Lee, R.J. , & Heilbrun, K. ( 2001). The prediction of criminal recidivism in juveniles. Criminal Justice and Behavior, 28, 367-394.

Currie, J. , & Yelowitz, A. ( 2000). Are public housing projects good for kids? Journal of Public Economics, 75, 9-104.

Dowden, C. , & Andrews, D.A. ( 1999). What works in young offender treatment: A meta-analysis . Forum on Corrections Research, 11, 21-24.

Elliott, D.S. , Wilson, W.J. , Huizinga, D. , Sampson, R.J. , Elliott, A. , & Rankin, B. ( 1996). The effects of neighborhood disadvantage on adolescent development. Journal of Research in Crime and Delinquency , 33, 389-426.

Hoge, R.D. , Andrews, D.A. , & Leschied, A. ( 2002). Youth Level of Service Case Management Inventory: YLS/CMI manual. Toronto, Canada: Multi-Health Systems.

10.

Jung, S. , & Rawana, E.P. ( 1999). Risk and need assessment of juvenile offenders. Criminal Justice and Behavior, 26, 69-89.

11.

Loeber, R. , & Dishion, T. ( 1983). Early predictors of male delinquency: A review. Psychological Bulletin, 94, 68-99.

12.

Lohmann, A. , & McMurran, G. (2009). Resident-defined neighborhood mapping: Using GIS to analyze phenomenological neighborhoods. Journal of Prevention & Intervention in the Community, 37, 66-81.

13.

Morenoff, J.D. , & Sampson, R.J. ( 1997). Violent crime and the spatial dynamics of neighborhood transition: Chicago, 1970-1990. Social Forces, 76, 31-64.

14.

Morenoff, J. , Sampson, R.J. , & Raudenbush, S. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology, 39, 517-560.

15.

Olver, M.E. , Stockdale, K.C. , & Wormith, J.S. ( 2009). Risk assessment with young offenders: A meta-analysis of the three assessment measures. Criminal Justice and Behavior , 36, 329-353.

16.

Onifade, E. , Davidson, W. , & Campbell, C. ( 2009). Risk assessment: The predictive validity of the Youth Level of Service Case Management Inventory with African Americans and girls . Journal of Ethnicity in Criminal Justice, 7, 205-221.

17.

Onifade, E.O. , Davidson, W. , Livsey, S. , Turke, G. , Horton, C. , & Malinowski, J. (2008). Risk assessment: Identifying patterns of risk in young offenders with the Youth Level of Service/Case Management Inventory. Journal of Criminal Justice, 36, 165-173.

18.

Pitts, J. ( 2001). Korrectional karaoke: New labour and the zombification of youth justice. Youth Justice, 1, 3-16.

19.

Raudenbush, S.W. , & Bryk, A.S. ( 2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

20.

Schmidt, F. , Hoge, R.D. , & Gomes, L. ( 2005). Reliability and validity analyses of the Youth Level of Service/Case Management Inventory. Criminal Justice and Behavior, 32, 329-344.

21.

Schumacher, M. , & Kurz-Gwen, A. ( 2000). The 8% solution: Preventing serious, repeat juvenile crime. Thousand Oaks, CA: Sage.

22.

Schwalbe, C.S. ( 2007). Risk assessment for juvenile justice: A meta-analysis . Law and Human Behavior, 31, 449-462.

23.

Schwalbe, C.S. ( 2008). A meta-analysis of juvenile justice risk assessment instruments: Predictive validity by gender. Criminal Justice and Behavior , 35, 1367-1381.

24.

Schwartz, A.E. , McCabe, B.J. , Ellen, I.G. , & Chellman, C.C. (2010). Public schools, public housing: The education of children living in public housing. Urban Affairs Review, 46, 68-89.

25.

Shaw, C.R. , & McKay, H.D. (1969). Juvenile delinquency and urban areas: A study of rates of delinquents in relation to differential characteristics of local communities in American cities (Rev. ed.). Chicago, IL: University of Chicago Press.

26.

Snyder, H.N. , & Sickmund, M. ( 2006). Juvenile offenders and victims: 2006 national report . Washington, DC: U.S. Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention.

27.

U.S. Census Bureau. (2008). State & county quickfacts. Retrieved from http://quickfacts.census.gov

28.

Webster, C. , MacDonald, R. , & Simpson, M. ( 2006). Predicting criminality? Risk factors, neighborhood influence and desistance. Youth Justice, 6, 7-22.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

7.06 MB