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Predictive Validity Performance Indicators in Violence Risk Assessment: A Methodological Primer
Author(s) -
Singh Jay P.
Publication year - 2013
Publication title -
behavioral sciences and the law
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.649
H-Index - 74
eISSN - 1099-0798
pISSN - 0735-3936
DOI - 10.1002/bsl.2052
Subject(s) - predictive validity , receiver operating characteristic , measure (data warehouse) , risk assessment , incremental validity , calibration , poison control , predictive value , criterion validity , test validity , statistics , computer science , risk analysis (engineering) , data mining , psychometrics , medicine , construct validity , mathematics , environmental health , computer security
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.