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Latent Class Analysis

  1. Jost Reinecke

Published Online: 30 JAN 2010

DOI: 10.1002/9780470479216.corpsy0497

Corsini Encyclopedia of Psychology

Corsini Encyclopedia of Psychology

How to Cite

Reinecke, J. 2010. Latent Class Analysis. Corsini Encyclopedia of Psychology. 1–2.

Author Information

  1. University of Bielefeld, Germany

Publication History

  1. Published Online: 30 JAN 2010

Abstract

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.