A statistical model can be called a latent class (LC) or mixture model if it assumes that some
of its parameters differ across unobserved subgroups, LCs, or mixture components. This …

Over the past several years more significant books have been published on latent class (LC)
and finite mixture models than any other class of statistical models. The recent increase in …

The basic idea underlying latent class (LC) analysis is a very simple one: some of the
parameters of a postulated statistical model differ across unobserved subgroups. These …

The latent class (LC) models that have been developed so far assume that observations are
independent. Parametric and nonparametric random–coefficient LC models are proposed …

Latent class (LC) modeling was initially introduced by Lazarsfeld and Henry (1968) as a way
of formulating latent attitudinal variables from dichotomous survey items. In contrast to factor …

Since 1995, more significant books have been published on latent class (LC) and finite
mixture models than any other class of statistical models. The recent increase in interest in …

This chapter discusses models with latent variables that are continuous and/or categorical. It
also gives an overview of modeling issues related to crosssectional analysis using latent …

Latent variable mixture models serve to investigate heterogeneous populations consisting of
two or more clusters of subjects. For instance, a population may consist of subjects who …

Since 1995, more significant books have been published on latent class (LC) and finite
mixture models than any other class of statistical models. The recent increase in interest in …

Latent class analysis is an analytic technique often used in educational and psychological
research to identify meaningful groups of individuals within a larger heterogeneous …