Criminal Careers: Discrete or Continuous?

David Greenberg

    Research output: Contribution to journalArticle

    Abstract

    Purpose: Numerous empirical studies of criminal careers have made use of finite mixture modeling to analyze sequences of events such as crimes or arrests. This paper aims to demonstrate that the analysis of criminal careers can benefit from the use of alternative methods, including multilevel methods, and individual time series. Methods: We use multilevel nonlinear modeling and individual time series techniques to analyze artificial data as well as arrest histories for 3432 males released from the California Youth Authority in 1981 and 1986, and followed for several decades after release. Results: Multilevel methods are capable of identifying discrete groups in longitudinal data. In the California Youth data set, we find little clear evidence of sharply discrete arrest trajectories. Conclusions: We recommend that researchers explore alternatives to finite mixture modeling when analyzing criminal career data.

    Original languageEnglish (US)
    Pages (from-to)5-44
    Number of pages40
    JournalJournal of Developmental and Life-Course Criminology
    Volume2
    Issue number1
    DOIs
    StatePublished - Mar 1 2016

    Fingerprint

    career
    time series
    Crime
    Sequence Analysis
    Research Personnel
    offense
    event
    history
    evidence
    Group

    Keywords

    • Criminal careers
    • Finite mixture modeling
    • Individual time eries
    • Latent growth curve modeling

    ASJC Scopus subject areas

    • Applied Psychology
    • Law
    • Life-span and Life-course Studies

    Cite this

    Criminal Careers : Discrete or Continuous? / Greenberg, David.

    In: Journal of Developmental and Life-Course Criminology, Vol. 2, No. 1, 01.03.2016, p. 5-44.

    Research output: Contribution to journalArticle

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