A critical examination of actuarial offender-based prediction assessments: guidance for the next generation of assessments
Connolly, Michele Moczygemba
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This study critically examines the prediction and classification aspect of the community supervision process. Probation departments across the United States, Canada and Europe use assessment instruments to attempt to predict who is likely to continue to engage in criminal behavior so that they can be classified and supervised accordingly. This study focuses on four fundamental questions: What is prediction and classification of offenders? Why are prediction and classification important? What do we know about the reliability and validity of prediction and classification applications? How can prediction and classification be improved? The methods of the study consists of constructing risk prediction models to compete against one of the most commonly used risk assessments in the field of community supervision: the Wisconsin risk and need assessment. Over thirty logistical regression models are constructed in an attempt to improve upon existing technology. Models are constructed for the outcomes rearrest, probation revocation and probation success. The findings of this study in no way diminish the need for accurate prediction and appropriate assessment. They do show that the predictive power of the most commonly used assessment instruments and instruments based on current data and methods is negligible and therefore should not be relied on as a sole factor in classification. Concluded is that significant improvement in offender risk prediction instruments will likely only be made if the specifications of the instruments become more closely linked with criminological theory. Utilizing a battery of assessments grounded in theory that take into account the offender’s characteristics and the community in which they reside, may be the only way we make progress in predicting their likelihood of future offending.