Latent Class Analysis
Published Online: 30 JAN 2010
Copyright © 2010 John Wiley & Sons, Inc. All rights reserved.
Corsini Encyclopedia of Psychology
How to Cite
Reinecke, J. 2010. Latent Class Analysis. Corsini Encyclopedia of Psychology. 1–2.
- Published Online: 30 JAN 2010
Latent class analysis (LCA) is a statistical method for finding subtypes of related cases (latent classes) from multivariate categorical data. Latent classes are the dimensions that structure the cases with respect to a set of observed variables. It is assumed that parameters of a statistical model differ across unobserved subgroups. These subgroups form the categories of a categorical latent variable. In principle, cases of a data set are divided into latent classes, which are so-called conditionally independent classes, meaning that the observed variables are uncorrelated within classes. With the estimated parameters, cases are classified according to their most likely latent class. In applications the LCA can be used to find types of attitude structures from survey responses, consumer segments from preference variables, and examinee subgroups from their answers to test items.