a method for finding discrete subgroups of related cases (
latent classes) from multivariate
categorical data. For example, a health researcher might use latent class analysis to determine into which one of 10 diagnostic categories to place each individual in a group on the basis of the presence or absence of several distinct symptoms. The procedure is analogous to
cluster analysis in that, from a given sample of cases measured on several variables, the researcher attempts to identify a smaller number of underlying dimensions upon which to classify them. The use of a categorical
latent variable, however, is what distinguishes latent class analysis from more traditional clustering approaches such as
factor analysis, which involve continuous latent variables. Also called
latent class modeling.