Assessing Associations Between Changes in Risk and Subsequent Reoffending: An Introduction to Relevant Statistical Models. (January 2017)

• Record Type: Journal Article
• Authors: Yang, Min; Guo, Boliang; Olver, Mark E.; Polaschek, Devon L. L.; Wong, Stephen C. P.
• Other Names: Helmus L. Maaike guest-editor.; Babchishin Kelly M. guest-editor.
• Is Part Of: Criminal justice and behavior. Volume 44:Number 1(2017:Jan.)
• Page Start: 59
• Page End: 84
• Publication Date: 2017-01
• Subjects: risk changes -- recidivism -- prediction -- multilevel models -- joint models; Correctional psychology -- Periodicals; 364.6
• DOI: 10.1177/0093854816678648
• Languages: English
• ISSNs: 0093-8548

Abstract

Research on recidivism prediction has made important advances, but the same cannot be said of research assessing relationships between risk changes over time or after treatment and subsequent reoffending. In realistic criminal justice situations, data linking changes in risk to recidivism are often fraught with problems due to missing data, irregular intervals in repeat risk assessments, and individual differences such as age and risk levels. Traditional statistical methodologies such as ANCOVA for repeated measures are not suited for analyzing data with these features. We presented four types of statistical modeling techniques that can effectively accommodate these noisier data: conventional regression, conditional regression, two-stage, and joint models. The two-stage models consist of multilevel growth model and conventional regression. The joint models refer to structural equational models. Two example data sets were used to illustrate the application of these methodologies.