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Prediction of Criminal Recidivism in Juveniles: A Meta-Analysis

NCJ Number
Criminal Justice and Behavior Volume: 28 Issue: 3 Dated: June 2001 Pages: 367-394
Cindy C. Cottle; Ria J. Lee; Kirk Heilbrun
Date Published
June 2001
28 pages
A meta-analysis was conducted to identify risk factors that best predict juvenile recidivism, defined as rearrest for offending of any kind.
A total of 23 published studies, representing 15,265 juveniles, met inclusion criteria for the study. Effect sizes were calculated for 30 predictors of recidivism. Eight groups of predictors were compared: demographic information, offense history, family and social factors, educational factors, intellectual and achievement scores, substance use history, clinical factors, and formal risk assessment. The results showed the strongest individual predictors to be a younger age at first commitment, younger age at first contact with the law, and a history of nonsevere pathology. In a broader sense, the domains of offense history and family and social factors were consistently associated with recidivism; whereas, other domains contained certain variables that were significant but were less consistent across the entire domain. Some of the variables identified as significant predictors of recidivism can be considered static, because they were not subject to change through planned intervention; nonetheless, static variables were potentially useful in the a priori identification of juvenile recidivism risk. The study also identified variables that could be considered dynamic, with a potential to change through planned intervention. Variables in the family and social domain may be considered dynamic risk factors, including family instability and problematic interactions, association with delinquent peers, and poor use of leisure time. Conduct problems, nonsevere pathologies, and substance abuse could also be considered dynamic. The demarcation of both static and dynamic risk factors for juveniles who had already been arrested at least once may promote the development of risk assessment tools that would allow both a priori classification of risk and the targeting of dynamic areas for risk-reducing intervention planning. 4 tables, 2 notes, and 54 references