PROC LCA: A SAS Procedure for Latent Class Analysis

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.

ACKNOWLEDGMENTS

This research was supported by National Institute on Drug Abuse Grants P50 DA 10075 and K05 DA 018206. We would like to thank Kari C. Kugler for helpful comments on a draft of this article and Bobby L. Jones for providing SAS programming expertise.

Notes

¹Copyright 2002–2003 SAS Institute, Inc. SAS and all other SAS Institute, Inc. product or service names are registered trademarks or trademarks of SAS Institute, Inc., Cary, NC, USA.

^a SEED statement is required only if the START option is not included in the PROC LCA statement.

²The fitting paradigm used for this model is based on an assumption of simple random sampling. Although data that do not meet this assumption can, and often are, used for latent class modeling, it is possible for the interpretation of one or more latent classes to change after the addition of covariates. If the ρ parameters change substantially with the addition of a covariate, there is evidence that the data are not representative of the population, and evidence of lack of model fit for the overall population. If this occurs, it might be worthwhile splitting the sample based on levels of the covariate and examining the latent class model separately for each subsample. Fitting latent class models in samples that are not simple random samples is a topic of current research (see, e.g., Asparouhov, 2005).