Multi-state Research on Juvenile Reoffending

Strategies for preventing recidivism and risk management of juvenile justice populations has received significant research and investment over the past three decades. Efforts to reduce contact with the justice system, (i.e., diversion) has significantly reduced the number of youths supervised nationally (OJJDP, 2020). Despite recent reductions in filings, supervision and the use of detention, youth of color continue to be overrepresented in all system areas (OJJDP, 2019). Further, policy concerns such as, age boundaries for both dependency and adult supervision, timing and administration of assessments, and responses to technical violations challenge the system. These operational challenges are coupled with the current reality that juvenile justice systems enter a great deal of data into systems but lack access to data systems for purposes of analysis and creation of standardized metrics. Creation of such systems can help guide and monitor youth/agency progress and guide state and federal responses to juvenile justice. In 2020, Hamilton and colleagues collected a 10-state sample of youth assessment and recidivism data. With nearly half a million assessments, advancement in understanding how patterns of risk and needs varied by state, gender, race/ethnicity, and outcome type were documented. The current study proposes to expand the 2020 sample, updating with new sites, cases, and measures. Using a nationally representative, 15-state sample of youth assessment and recidivism data, UNO proposes to examine four research questions: RQ1: What factors predict juvenile re-offending, generally and do patterns differ by subgroup? This question selects assessment items predictive of reoffending. We will identify specified prediction patterns by demographics, family structure, state, and supervision and offense types. RQ2: Do reoffending patterns vary by outcome? Using collected offending history data, we model frequency, offense type, time to reoffending, while controlling for time on supervision. RQ3: What policies and practices impact reoffending? Categorizing sites by stipulated age boundaries, timing of assessment/reassessments, programming access, and responses to technical violations, we model impacts of policies and practices on reoffending. RQ4: What factors moderate reoffending? Here we will incorporate moderating items (i.e., employment, mental health) and domain score changes to model youth growth over time, and environmental (state-level) moderators (i.e., crime rates, median income, poverty) will be assessed. Finally, developed training products will facilitate efficient collection, linkage, and analysis. Specifically, a web-based application provides interactive ‘dashboards’ to assist agency visualization of youth profiles and trends. Training materials will further establish comparative and standardized metrics of youth success. CA/NCF