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Latent Markov Modelling of Recidivism Data

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  • Catrien C.J.H. Bijleveld
  • Ab Mooijaart

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.

Suggested Citation

  • Catrien C.J.H. Bijleveld & Ab Mooijaart, 2003. "Latent Markov Modelling of Recidivism Data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(3), pages 305-320, August.
    • Handle: RePEc:bla:stanee:v:57:y:2003:i:3:p:305-320
    DOI: 10.1111/1467-9574.00233
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    References listed on IDEAS

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    1. Vermunt, J.K., 1993. "lEM : Log-linear and event history analysis with missing data using the EM algorithm," WORC Paper 93.09.015/7, Tilburg University, Work and Organization Research Centre.
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    Cited by:

    1. Siem Jan Koopman & Marius Ooms & André Lucas & Kees van Montfort & Victor Van Der Geest, 2008. "Estimating systematic continuous‐time trends in recidivism using a non‐Gaussian panel data model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 62(1), pages 104-130, February.
    2. Cees H. Elzinga & Adriaan W. Hoogendoorn & Wil Dijkstra, 2007. "Linked Markov Sources," Sociological Methods & Research, , vol. 36(1), pages 26-47, August.
    3. Francesco Bartolucci & Fulvia Pennoni & Brian Francis, 2007. "A latent Markov model for detecting patterns of criminal activity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 115-132, January.
    4. Eugene C.X. Ikejemba & Peter C. Schuur, 2018. "Analyzing the Impact of Theft and Vandalism in Relation to the Sustainability of Renewable Energy Development Projects in Sub-Saharan Africa," Sustainability, MDPI, vol. 10(3), pages 1-17, March.

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