Group-based Criminal Trajectory Analysis Using Cross-validation Criteria

In this article, we discuss the challenge of determining the number of classes in a family of finite mixture models with the intent of improving the specification of latent class models for criminal trajectories. We argue that the traditional method of using either the Proc Traj or Mplus package to compute and maximize the Bayesian Information Criterion (BIC) is problematic: Proc Traj and Mplus do not always compute the MLE (and hence the BIC) accurately, and furthermore, BIC on its own does not always indicate a reasonable-seeming number of groups even when computed correctly. As an alternative, we propose the new freely available software package, crimCV, written in the R-programming language, and the methodology of cross-validation error (CVE) to determine the number of classes in a fair and reasonable way. In this article, we apply the new methodology to two samples of N = 378 and N = 386 male juvenile offenders whose criminal behavior was tracked from late childhood/early adolescence into adulthood. ...