PROC LCA: A SAS Procedure for Latent Class Analysis

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 acr...

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Bibliographic Details
Published in:Structural equation modeling 2007-01, Vol.14 (4), p.671-694
Main Authors: Lanza, Stephanie T., Collins, Linda M., Lemmon, David R., Schafer, Joseph L.
Format: Article
Language:English
Subjects:
Alcohol use
Behavior
Drinking
High School Seniors
Item Response Theory
Multivariate analysis
Probability
Secondary school students
Statistical Analysis
Structural Equation Models
Syntax
Variables
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Summary: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.
ISSN:1070-5511
1532-8007
DOI:10.1080/10705510701575602