Using data augmentation to obtain standard errors and conduct hypothesis tests in latent class and latent transition analysis

Stephanie T. Lanza, Linda M. Collins, Joseph L. Schafer, Brian P. Flaherty

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

Latent class analysis (LCA) provides a means of identifying a mixture of subgroups in a population measured by multiple categorical indicators. Latent transition analysis (LTA) is a type of LCA that facilitates addressing research questions concerning stage-sequential change over time in longitudinal data. Both approaches have been used with increasing frequency in the social sciences. The objective of this article is to illustrate data augmentation (DA), a Markov chain Monte Carlo procedure that can be used to obtain parameter estimates and standard errors for LCA and LTA models. By use of DA it is possible to construct hypothesis tests concerning not only standard model parameters but also combinations of parameters, affording tremendous flexibility. DA is demonstrated with an example involving tests of ethnic differences, gender differences, and an Ethnicity X Gender interaction in the development of adolescent problem behavior.

Original languageEnglish (US)
Pages (from-to)84-100
Number of pages17
JournalPsychological Methods
Volume10
Issue number1
DOIs
StatePublished - Mar 2005

All Science Journal Classification (ASJC) codes

  • Psychology (miscellaneous)

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