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Predictive Validity Performance Indicators in Violence Risk Assessment: A Methodological Primer


  • Jay P. Singh Ph.D.

    Corresponding author
    1. Institute of Health Sciences, Molde University College, Molde, Norway
    • Department of Mental Health Law and Policy, University of South Florida, Tampa, FL, U.S.A.
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Correspondence to: Jay P. Singh, Ph.D., Department of Mental Health Law and Policy, University of South Florida, 13301 Bruce B. Downs Blvd., Tampa, FL 33612, U.S.A. E-mail:


The predictive validity of violence risk assessments can be divided into two components: calibration and discrimination. The most common performance indicator used to measure the predictive validity of structured risk assessments, the area under the receiver operating characteristic curve (AUC), measures the latter component but not the former. As it does not capture how well a risk assessment tool's predictions of risk agree with actual observed risk, the AUC provides an incomplete portrayal of predictive validity. This primer provides an overview of calibration and discrimination performance indicators that measure global performance, performance in identifying higher-risk groups, and performance in identifying lower-risk groups. It is recommended that future research into the predictive validity of violence risk assessment tools includes a number of performance indicators that measure different facets of predictive validity and that the limitations of reported indicators be routinely explicated. Copyright © 2013 John Wiley & Sons, Ltd.