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A cross-validation of the Violence Risk Appraisal Guide-Revised (VRAG-R) within a correctional sample.
Anthony J J Glover1, Frances P Churcher2, Andrew L Gray3
1Mental Health Services, Correctional Service Canada.
Law and Human Behavior
|August 8, 2017
Summary
The Violence Risk Appraisal Guide-Revised (VRAG-R) shows moderate predictive validity for assessing violence risk in male offenders over 22 years. This tool aids in evaluating recidivism risk among correctional populations.
Area of Science:
- Forensic Psychology
- Criminology
- Psychometrics
Background:
- The Violence Risk Appraisal Guide-Revised (VRAG-R) was developed to improve upon the original VRAG.
- Updated and larger sample sizes with extended follow-up periods informed its development.
Purpose of the Study:
- To examine the interrater reliability and predictive validity of the VRAG-R.
- To compare the VRAG-R's performance against other risk assessment tools.
- To assess the VRAG-R's utility in predicting general and violent recidivism in adult male correctional offenders.
Main Methods:
- A sample of 120 adult male correctional offenders was utilized.
- Interrater reliability, predictive validity, and comparative validity were assessed.
- A follow-up period of up to 22 years postrelease was employed.
- Time-dependent area under the curve (AUC) analysis was used to evaluate sustained predictive validity.
Main Results:
- The VRAG-R demonstrated moderate predictive validity for both general and violent recidivism.
- This predictive validity was sustained over the 22-year follow-up period.
- Moderate predictive validity was observed even when the Antisociality item was modified.
- Analysis indicated that only a few individual items significantly predict violent recidivism.
Conclusions:
- The VRAG-R is a moderately effective tool for predicting violent recidivism in correctional offenders.
- The findings have practical implications for the application of the VRAG-R in risk assessment.
- Further research may explore the contribution of individual items to overall predictive accuracy.
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