The Effect of Missing Item Data on the Relative Predictive Accuracy of Intimate Partner Violence Risk Assessment Tools.

Abstract

In an innovative simulation study, Perley-Robertson et al. found that two correctional risk assessment tools were robust to missing data, with summation, proration, and multiple imputation producing nearly identical relative predictive validity results. However, the uniform deletion of items across cases may have preserved their risk rankings and, consequently, relative predictive accuracy. We extend this research by applying identical missing data conditions (1%-50% of items deleted in 10% increments) to one third, two thirds, and three thirds of a high-risk intimate partner violence (IPV) sample assessed on the Ontario Domestic Assault Risk Assessment (ODARA) and Spousal Assault Risk Assessment-Version 2 (SARA-V2; N = 267). Neither missing data nor the handling method affected relative predictive accuracy, though summation underestimated absolute risk. These findings support proration or multiple imputation when IPV risk scale items are missing within a research sample, and underscore that proration is preferable to summed totals in practice.