Dataset Information

Modeling Criminal Careers as Departures from a Unimodal Population Age-Crime Curve: The Case of Marijuana Use.

ABSTRACT: A major aim of longitudinal analyses of life course data is to describe the within- and between-individual variability in a behavioral outcome, such as crime. Statistical analyses of such data typically draw on mixture and mixed-effects growth models. In this work, we present a functional analytic point of view and develop an alternative method that models individual crime trajectories as departures from a population age-crime curve. Drawing on empirical and theoretical claims in criminology, we assume a unimodal population age-crime curve and allow individual expected crime trajectories to differ by their levels of offending and patterns of temporal misalignment. We extend Bayesian hierarchical curve registration methods to accommodate count data and to incorporate influence of baseline covariates on individual behavioral trajectories. Analyzing self-reported counts of yearly marijuana use from the Denver Youth Survey, we examine the influence of race and gender categories on differences in levels and timing of marijuana smoking. We find that our approach offers a flexible model for longitudinal crime trajectories and allows for a rich array of inferences of interest to criminologists and drug abuse researchers.

SUBMITTER: Telesca D

PROVIDER: S-EPMC3913486 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Modeling Criminal Careers as Departures from a Unimodal Population Age-Crime Curve: The Case of Marijuana Use.

Telesca Donatello D Erosheva Elena A EA Kreager Derek A DA Matsueda Ross L RL

Journal of the American Statistical Association 20120101 500

A major aim of longitudinal analyses of life course data is to describe the within- and between-individual variability in a behavioral outcome, such as crime. Statistical analyses of such data typically draw on mixture and mixed-effects growth models. In this work, we present a functional analytic point of view and develop an alternative method that models individual crime trajectories as departures from a population age-crime curve. Drawing on empirical and theoretical claims in criminology, we ...[more]

PMID: 24504416

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Project description:BACKGROUND:Cross-sectional and retrospective offence data are often used to classify sex offenders in epidemiological and survey research, but little empirical evidence exists regarding the practical implications of this for applied research. This study describes the classification of sex offenders from a cohort of prisoners recruited as part of an Australian inmate health survey and the implications for reporting results. METHODS:Data-linkage was used to join the New South Wales (NSW) Inmate Health Surveys to the states re-offending database to identify men with histories of sexual offending. Sex offenders were classified into men who sexually offended against children only (ChildSOs), against adults only (AdultSOs), and men who sexually offended against both children and adults (Age-PolySOs). RESULTS:Using historical offending data rather than the current offence information only, an additional 35.4% of men with histories of sexual offences were identified. Differences were found between the three sex offender subgroups in terms of demographic characteristics, health, and criminal careers. Age-PolySOs reported higher educational attainment, were less likely to report being self-employed, single marital status, and having children. Half the ChildSOs self-reported a mental health issue and half of the ChildSOs and Age-PolySOs reported four or more chronic health conditions. Age-PolySOs were older than the other sex offender groups when committing their first non-sexual, non-violent crime (M?=?43.2?years, SD?=?13.8); violent crime (M?= 39.5?years, SD?=?11.1); and sexual crime (M?= 47.8?years, SD?=?11.2). Age-PolySOs also committed more sexual offences (M?= 5.91, SD?=?11.2) compared to those who only offended against one victim age group. CONCLUSION:These findings suggested that historical offending records should be used to more accurately identify sex offender subgroups and that differences in demographic, health, and criminal careers exist for the different sex offender subgroups.

Dataset Information

Modeling Criminal Careers as Departures from a Unimodal Population Age-Crime Curve: The Case of Marijuana Use.

Publications

Modeling Criminal Careers as Departures from a Unimodal Population Age-Crime Curve: The Case of Marijuana Use.

Similar Datasets

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