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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Modelling escalation in crime seriousness
T2 - a latent variable approach
AU - Francis, Brian
AU - Liu, Jiayi
N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s40300-015-0073-4
PY - 2015/8
Y1 - 2015/8
N2 - This paper investigates the use of latent variable models in assessing escalationin 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.
AB - This paper investigates the use of latent variable models in assessing escalationin 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.
KW - Escalation
KW - aggrevation
KW - Longitudinal data analysis
KW - latent variables
KW - heterogeneity
KW - group-based trajectory modelling
KW - growth mixture modelling
KW - Criminal careers
KW - comparative study
U2 - 10.1007/s40300-015-0073-4
DO - 10.1007/s40300-015-0073-4
M3 - Journal article
VL - 73
SP - 277
EP - 297
JO - Metron
JF - Metron
SN - 0026-1424
IS - 2
ER -