Suchergebnisse

This article discusses the application of latent Markov modelling for the analysis of recidivism data. We briefly examine the relations of Markov modelling with log–linear analysis, pointing out pertinent differences as well. We show how the restrictive Markov model may be more easily applicable by adding latent variables to the model, in which case the latent Markov model is a dynamic version of the latent class model. As an illustration, we apply latent Markov analysis on an empirical data set of juvenile prosecution careers, showing how the Markov analyses producing well‐fitting and interpretable solutions. We end by comparing the possible contributions of Markov modelling in recidivism research, outlining its drawbacks as well. Recommendations and directions for future research conclude the article.

We discuss structural equation models for non‐normal variables. In this situation the maximum likelihood and the generalized least‐squares estimates of the model parameters can give incorrect estimates of the standard errors and the associated goodness‐of‐fit chi‐squared statistics. If the sample size is not large, for instance smaller than about 1000, asymptotic distribution‐free estimation methods are also not applicable. This paper assumes that the observed variables are transformed to normally distributed variables. The non‐normally distributed variables are transformed with a Box–Cox function. Estimation of the model parameters and the transformation parameters is done by the maximum likelihood method. Furthermore, the test statistics (i.e. standard deviations) of these parameters are derived. This makes it possible to show the importance of the transformations. Finally, an empirical example is presented.