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Latent Markov Modelling of Recidivism Data
Author(s) -
Bijleveld Catrien C.J.H.,
Mooijaart Ab
Publication year - 2003
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/1467-9574.00233
Subject(s) - recidivism , markov model , latent class model , markov chain , computer science , variable order markov model , econometrics , markov process , latent variable model , set (abstract data type) , variable order bayesian network , machine learning , statistics , mathematics , artificial intelligence , psychology , bayesian probability , bayesian inference , criminology , programming language
This article discusses the application of latent Markov modelling for the analysis of recidivism data. We briefly examine the relations of Markov modelling with log–linear analysis, pointing out pertinent differences as well. We show how the restrictive Markov model may be more easily applicable by adding latent variables to the model, in which case the latent Markov model is a dynamic version of the latent class model. As an illustration, we apply latent Markov analysis on an empirical data set of juvenile prosecution careers, showing how the Markov analyses producing well‐fitting and interpretable solutions. We end by comparing the possible contributions of Markov modelling in recidivism research, outlining its drawbacks as well. Recommendations and directions for future research conclude the article.