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

ObjectivesPrior 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 b...

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Detalles Bibliográficos
Autor principal: Stone, Ariel G. (Autor)
Otros Autores: Lloyd, Caleb ; Spivak, Benjamin L. ; Papalia, Nina L. ; Serin, Ralph C.
Tipo de documento: Electrónico Artículo
Lenguaje:Inglés
Publicado: 2024
En: Journal of quantitative criminology
Año: 2024, Volumen: 40, Número: 2, Páginas: 215-255
Acceso en línea: Volltext (kostenfrei)
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Palabras clave:
Class
Criminology
Criminology Theory
Dynamic risk factors
Ethnicity
Gender and Crime
Group trajectory modelling
Joint latent class models
Quantitative Criminology
Re-entry
Recidivism
Recidivism prediction
Risk Factors
Descripción
Sumario:ObjectivesPrior 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.MethodsThis 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.ResultsWe 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.ConclusionsThere 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.
ISSN:1573-7799
DOI:10.1007/s10940-022-09566-5