Static Risk Offender Need Guide for Recidivism

Hamilton, Zachary; Kigerl, Alex; Campagna, Michael; Barnoski, Robert; Wormer, Jacqueline van; Block, Lauren M.

doi:10.1037/t59687-000

Cited by 3 publications

(2 citation statements)

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“…Relatedly, others have noted the potential of machine learning techniques possessing the ability to outperform the regression based models utilized here (Berk & Bleich, 2013). Our prior findings with Washington State offender risk assessment data did not find machine learning models (e.g., random forests and neural networks) to provide improved performance (Hamilton et al, 2014). Generally, we found comparable, and often improved, performance from regression methods, which ultimately guided our decision to avoid machine learning techniques for STRONG-R model creation.…”

Section: Discussionmentioning

confidence: 90%

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Hamilton¹,

Kigerl²,

Campagna³

et al. 2015

Criminal Justice and Behavior

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Recidivism risk assessment tools have been utilized for decades. Although their implementation and use have the potential to touch nearly every aspect of the correctional system, the creation and examination of optimal development methods have been restricted to a small group of instrument developers. Furthermore, the methodological variation among common instruments used nationally is substantial. The current study examines this variation by reviewing methodologies used to develop several existing assessments and then tests a variety of design variations in an attempt to isolate and select those which provide improved content and predictive performance using a large sample (N = 44,010) of reentering offenders in Washington State. Study efforts were completed in an attempt to isolate and identify potential incremental performance achievements. Findings identify a methodology for improved prediction model performance and, in turn, describe the development and introduction of the Washington State Department of Correction’s recidivism prediction instrument—the Static Risk Offender Need Guide for Recidivism (STRONG-R).

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Section: Discussionmentioning

confidence: 90%

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Hamilton¹,

Kigerl²,

Campagna³

et al. 2015

Criminal Justice and Behavior

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“…Earlier research has demonstrated that the PACT has moderate predictive utility across racial groups, gender, type of referral event (e.g., misdemeanor, felony), and jurisdictions (Baglivio, 2009(Baglivio, , 2015Baglivio & Jackowski, 2013;Hamilton et al, 2015;Martin, 2012;Winokur-Early et al, 2012). The current study is novel due to the fact it is the second study besides Hutchins's (2019) research to explore the PACT pre-screen assessment's ability to predict time till recidivism among a sample of Texas juvenile justice-involved youth, and the first do so by tracking subjects for 36 months rather than 12.…”

Section: Current Studymentioning

confidence: 99%

Revalidating the Positive Achievement Change Tool (PACT) in a Major City: A Survival Analysis

Mueller¹,

Carey

Noh

2022

Crime & Delinquency

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The Positive Achievement Change Tool (PACT) is a popular risk-assessment tool used in juvenile justice systems across the country. This study is an effort at revalidating the PACT in the context of a large Southern city by utilizing days to recidivism in the context of survival analysis. The current research is concerned with whether the PACT’s risk classifications are effective in predicting recidivism. Findings indicate that the PACT effectively predicts recidivism better than chance and that recidivism is especially common early on in probation, with most incidents taking place within the first 200 days. However, the predictive validity of the PACT is not strong for the study population, and its implementation may benefit from context-specific reforms.

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Validating Drug Court Collaboration Instrument: Differences in Model Adherence Between Adult and Juvenile Drug Courts

Mei

Wormer

et al. 2019

Int J Offender Ther Comp Criminol

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Drug courts aim to significantly address drug abuse and drug-related criminality. However, the effectiveness of drug courts varies from court to court. The variation of success demands insights regarding what is going on inside the “black box” of drug court practices. Therefore, it is necessary to evaluate to what extent drug courts are operated in adherence with guiding principles and strategies. Using a national sample and validated measures, the current article examines the “black boxes” of adult and juvenile drug courts across the country. We found that, in general, adult drug courts face less model adherence challenges in comparison with juvenile courts, which may, in part, explain why adult drug courts perform better than juvenile drug courts overall.

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Static Risk Offender Need Guide for Recidivism

Cited by 3 publications

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Revalidating the Positive Achievement Change Tool (PACT) in a Major City: A Survival Analysis

Validating Drug Court Collaboration Instrument: Differences in Model Adherence Between Adult and Juvenile Drug Courts

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