Latent Markov modeling of recidivism data

C.C.J.H. Bijleveld, A. Mooijaart

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)305-320
Number of pages16
JournalStatistica Neerlandica. Journal of the Netherlands Society for Statistics and Operations Research
Issue number57
DOIs
Publication statusPublished - 2003

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