What is Latent Class Analysis?
Latent Class Analysis (LCA) is a statistical method for finding subtypes of related cases (latent classes) from multivariate categorical data. For example, it can be used to find distinct diagnostic categories given presence/absence of several symptoms, types of attitude structures from survey responses, consumer segments from demographic and preference variables, or examinee subpopulations from their answers to test items. The results of LCA can also be used to classify cases to their most likely latent class. LCA is used in way analogous to cluster analysis (see FAQ, How does LCA compare to other statistical methods?). That is, given a sample of cases (subjects, objects, respondents, patients, etc.) measured on several variables, one wishes to know if there is a small number of basic groups into which cases fall. A more precise analogy is between LCA and a type of cluster analysis called finite mixture estimation (Day, 1969; Titterington, Smith & Makov, 1985; Wolfe, 1970).
