Criminal Careers: Discrete or Continuous?

Abstract

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. 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. 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. We recommend that researchers explore alternatives to finite mixture modeling when analyzing criminal career data.