Assessing Violence Risk in Stalking Cases: A Regression Tree Approach.

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Abstract
Advances in the field of risk assessment have highlighted the importance of developing and validating models for problematic or unique subgroups of individuals. Stalking offenders represent one such subgroup, where fears of and potential for violence are well-known and have important implications for safety management. The present study applies a Classification and Regression Tree (CART) approach to a sample of stalking offenders in order to help further the process of identifying and understanding risk assessment strategies. Data from 204 stalking offenders referred for psychiatric evaluation to a publicly-funded clinic were used to develop and assess putative risk factors. A series of nested models were used to generate tree algorithms predicting violence in this sample of offenders. Both simplified and more extensive models generated high levels of predictive accuracy that were roughly comparable to logistic regression models but much more straightforward to apply in clinical practice. Jack-knifed cross-validation analyses demonstrated considerable shrinkage in the CART, although the models were still comparable to many other actuarial risk assessment instruments. Logistic regression models were much more resilient to cross-validation, with relatively modest loss in predictive power.