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 language | English (US) |
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Pages (from-to) | 84-100 |
Number of pages | 17 |
Journal | Psychological Methods |
Volume | 10 |
Issue number | 1 |
DOIs | |
State | Published - Mar 2005 |
Keywords
- Data augmentation
- Latent class analysis
- Latent transition analysis
- Latent variable model
- Multiple imputation
ASJC Scopus subject areas
- Psychology (miscellaneous)