DECISIONS LEADING TO INEQUALITIES IN THE JUVENILE JUSTICE SYSTEM: USING DATA TO PREDICT DELINQUENT OUTCOMES, INFORM DECISIONS, AND REDUCE DISPARITIES FOR JUSTICE-INVOLVED YOUTH

This dissertation focuses on how decisions are made in the juvenile justice system and what strategies can be used to make more informed decisions in the future, in three separate investigations. (1) To begin, eight years of initial detention decisions (N=26,128) were collected to determine what factors affect the arresting officers’ decision to detain youth at arrest. Findings reveal that the severity of current offense and prior offending history are the greatest predictors of initial detention. However, results demonstrate that race and the distance from a detention facility also influence these decisions. Non-white youth are more likely detained than their white counterpart holding relevant factors constant. Additionally, the closer an arrest takes place to a detention facility the more likely a youth is detained. This geographical variable is unique to this analysis and is found to mediate the effect that other geographic variables have on detention decisions (rural/urban classification). Also, evidence suggests that there is a greater disparity in the use of initial detention between white and non-white youth in areas that are closer in proximity to a detention facility. (2) Next, this investigation utilized six years of juvenile intakes (N=3,121) to create and validate a juvenile risk screener to predict recidivism in a one year period of risk. Items included in the risk screener were selected based on a series of bivariate and multivariate analyses. Out of the 246 risk factors eligible for the screener, seven factors were found to be important predictors for recidivism and were included in the risk screener. Validation measurements demonstrate comparable, and at times increased, prediction accuracy from the currently used, significantly larger risk assessment. (3) The final study analyzes an experimental method of weighting risk factors using conjunctive analysis of case configuration (CACC). This study demonstrates the importance of risk factor combinations and how these combinations affect the outcome. Using this weighting strategy, results found slight improvement in prediction accuracy over a simpler 0, 1 scoring method and a more complex score from logistic regression