ABSTRACT
Latent Class Analysis (LCA) is a statistical method for identifying unobserved groups based on categorical data. LCA is related to cluster analysis (see Chapter 4, this volume) in that both methods are concerned with the classification of cases (e.g., people or objects) into groups that are not known or specified a priori. In LCA, cases with similar response patterns on a series of manifest variables are classified into the same latent class although membership in latent classes is probabilistic rather than deterministic. In addition, LCA can be viewed as analogous to factor analysis (see Chapter 8, this volume), with the former examining categorical variables and the latter continuous ones; however, this comparison is less direct than in the case of cluster analysis. Although both LCA and factor analysis utilize manifest variables to gain insights into latent constructs, the focus of conventional factor analysis is on the structure of the variables as opposed to the structure of the cases. However, both LCA and factor analysis are based on the principle that observed variables are (conditionally) independent assuming knowledge of the latent structure. Finally, LCA is related to item response theory (see Chapter 12, this volume) and can be viewed as a generalization of discrete response models such as the Rasch model (Lindsay, Clogg, & Grego, 1991).
