PROC LCA: A SAS procedure for latent class analysis

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

doi:10.1080/10705510701575602

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 journal › Article › peer-review

872 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 language	English (US)
Pages (from-to)	671-694
Number of pages	24
Journal	Structural Equation Modeling
Volume	14
Issue number	4
DOIs	https://doi.org/10.1080/10705510701575602
State	Published - 2007

All Science Journal Classification (ASJC) codes

General Decision Sciences
General Economics, Econometrics and Finance
Sociology and Political Science
Modeling and Simulation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1080/10705510701575602

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title = "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.",

author = "Lanza, {Stephanie T.} and Collins, {Linda M.} and Lemmon, {David R.} and Schafer, {Joseph L.}",

note = "Funding Information: 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. SAS programming expertise.",

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doi = "10.1080/10705510701575602",

language = "English (US)",

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T1 - PROC LCA

T2 - A SAS procedure for latent class analysis

AU - Lanza, Stephanie T.

AU - Collins, Linda M.

AU - Lemmon, David R.

AU - Schafer, Joseph L.

N1 - Funding Information: 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. SAS programming expertise.

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

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PROC LCA: A SAS procedure for latent class analysis

Abstract

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