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Modelling escalation in crime seriousness: a latent variable approach

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  • Brian Francis
  • Jiayi Liu

Abstract

This paper investigates the use of latent variable models in assessing escalation in crime seriousness. It has two aims. The first is to contrast a mixed-effects approach to modelling crime escalation with a latent variable approach. The paper therefore examines whether there are specific subgroups of offenders with distinct seriousness trajectory shapes. The second is methodological—to compare mixed-effects modelling used in previous work on escalation with group-based trajectory modelling and growth mixture modelling (mixture of mixed-effects models). The availability of software is an issue, and comparisons of fit across software packages is not straightforward. We suggest that mixture models are necessary in modelling crime seriousness, that growth mixture models rather than group-based trajectory models provide the best fit to the data, and that R gives the best software environment for comparing models. Substantively, we identify three latent groups, with the largest group showing crime seriousness increases with criminal justice experience (measured through number of conviction occasions) and decreases with increasing age. The other two groups show more dramatic non-linear effects with age, and non-significant effects of criminal justice experience. Policy considerations of these results are briefly discussed. Copyright Sapienza Università di Roma 2015

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  • Brian Francis & Jiayi Liu, 2015. "Modelling escalation in crime seriousness: a latent variable approach," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 277-297, August.
    • Handle: RePEc:spr:metron:v:73:y:2015:i:2:p:277-297
    DOI: 10.1007/s40300-015-0073-4
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    References listed on IDEAS

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    5. Jacqmin-Gadda, Helene & Sibillot, Solenne & Proust, Cecile & Molina, Jean-Michel & Thiebaut, Rodolphe, 2007. "Robustness of the linear mixed model to misspecified error distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 5142-5154, June.
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    Cited by:

    1. Marco Alfó & Francesco Bartolucci, 2015. "Latent variable models for the analysis of socio-economic data," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 151-154, August.
    2. Yannick V. Markhof, 2020. "Divide to Conquer? Latent Preference Types and Country-level Heterogeneity," CSAE Working Paper Series 2020-05, Centre for the Study of African Economies, University of Oxford.
    3. Suonpää, Karoliina & Aaltonen, Mikko & van der Geest, Victor, 2020. "Crime and income trajectories preceding lethal and non-lethal violence," Journal of Criminal Justice, Elsevier, vol. 68(C).

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