Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al, 1997 doi:10.1080/01621459.1997.10473658; Clogg and Goodman, 1985 doi:10.2307/270847) and nonparametric random-effect LCA (Vermunt, 2003 doi:10.1111/j.0081-1750.2003.t01-1-00131.x).
Latent class analysis (LCA) is one of the most popular discrete mixture models for classifying individuals based on their responses to multiple manifest items. When there are existing subgroups in the data representing different populations, researchers are often interested in comparing certain aspects of latent class structure across these groups in LCA approach. In multiple-group LCA models, individuals are dependent owing to multilevel data structure, where observation units (i.e., individuals) are nested within a higher-level unit (i.e., group). This paper describes the implementation of multiple-group LCA in the R package
glca for exploring differences in latent class structure between populations, taking multilevel data structure into account. The package
glca deals with the fixed effect LCA and the random effect LCA; the former can be applied in the situation where populations are segmented by the observed group variable itself, whereas the latter can be used when there are too many levels in the group variable to make a meaningful group comparisons.