Trajectories of Change in Acute Dynamic Risk Ratings and Associated Risk for Recidivism in Paroled New Zealanders: A Joint Latent Class Modelling Approach

• Published in: Journal of quantitative criminology Vol. 40; no. 2; pp. 215 - 255
• Main Authors: Stone, Ariel G., Lloyd, Caleb D., Spivak, Benjamin L., Papalia, Nina L., Serin, Ralph C.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-06-2024; Springer Nature B.V
• Subjects: Change agents; Changes; Classism; Community; Criminal justice; Criminology; Criminology and Criminal Justice; Discrimination; Groups; Heterogeneity; Imprisonment; Intervention; Law and Criminolgy; Membership; Methodology of the Social Sciences; Original Paper; Parole; Recidivism; Rehabilitation; Risk assessment; Risk factors; Social structure; Sociology; Statistics; Supervision; Joint latent class models; Re-entry; Recidivism prediction; Group trajectory modelling; Dynamic risk factors
• ISSN: 0748-4518; 1573-7799
• DOI: 10.1007/s10940-022-09566-5

Objectives
Prior studies indicate risk for recidivism declines with time spent in the community post-incarceration. The current study tested whether declines in risk scores occurred uniformly for all individuals in a community corrections sample or whether distinct groups could be identified on the basis of similar trajectories of change in acute risk and time to recidivism. We additionally tested whether accounting for group heterogeneity improved prospective prediction of recidivism.
Methods
This study used longitudinal, multiple-reassessment data gathered from 3,421 individuals supervised on parole in New Zealand (
N
 = 92,104 assessments of theoretically dynamic risk factors conducted by community corrections supervision officers). We applied joint latent class modelling (JLCM) to model group trajectories of change in acute risk following re-entry while accounting for data missing due to recidivism (i.e., missing not at random). We compared accuracy of dynamic predictions based on the selected joint latent class model to an equivalent joint model with no latent class structure.
Results
We identified four trajectory groups of acute dynamic risk. Groups were consistently estimated across a split sample. Trajectories differed in direction and degree of change but using the latent class structure did not improve discrimination when predicting recidivism.
Conclusions
There may be significant heterogeneity in how individuals’ assessed level of acute risk changes following re-entry, but determining risk for recidivism should not be based on probable group membership. JLCM revealed heterogeneity in early re-entry unlikely to be observed using traditional analytic approaches.