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Modelling escalation in crime seriousness: a latent variable approach

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Modelling escalation in crime seriousness : a latent variable approach. / Francis, Brian; Liu, Jiayi.

In: Metron, Vol. 73, No. 2, 08.2015, p. 277-297.

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Francis, Brian ; Liu, Jiayi. / Modelling escalation in crime seriousness : a latent variable approach. In: Metron. 2015 ; Vol. 73, No. 2. pp. 277-297.

Bibtex

@article{9f4b6aa45ad341d68597323604dbbcdf,
title = "Modelling escalation in crime seriousness: a latent variable approach",
abstract = "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.",
keywords = "Escalation, aggrevation, Longitudinal data analysis, latent variables , heterogeneity, group-based trajectory modelling, growth mixture modelling, Criminal careers, comparative study ",
author = "Brian Francis and Jiayi Liu",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/s40300-015-0073-4 ",
year = "2015",
month = aug
doi = "10.1007/s40300-015-0073-4",
language = "English",
volume = "73",
pages = "277--297",
journal = "Metron",
issn = "0026-1424",
publisher = "Universita di Roma {"}La Sapienza{"}",
number = "2",

}

RIS

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 -