PROC LCA: A SAS procedure for latent class analysis

Stephanie T. Lanza, Linda M. Collins, David R. Lemmon, Joseph L. Schafer

Research output: Contribution to journalArticlepeer-review

550 Scopus citations

Abstract

Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables. In multiple-group LCA, both the measurement part and structural part of the model can vary across groups, and measurement invariance across groups can be empirically tested. LCA with covariates extends the model to include predictors of class membership. In this article, we introduce PROC LCA, a new SAS procedure for conducting LCA, multiple-group LCA, and LCA with covariates. The procedure is demonstrated using data on alcohol use behavior in a national sample of high school seniors.

Original languageEnglish (US)
Pages (from-to)671-694
Number of pages24
JournalStructural Equation Modeling
Volume14
Issue number4
DOIs
StatePublished - 2007

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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