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- Publisher
- American Psychological Association
- Copyright
- Copyright © 2013 American Psychological Association
- ISSN
- 1082-989x
- eISSN
- 1939-1463
- DOI
- 10.1037/a0031609
- pmid
- 23527607
- Publisher site
- See Article on Publisher Site
Abstract
Growth mixture modeling (GMM) represents a technique that is designed to capture change over time for unobserved subgroups (or latent classes) that exhibit qualitatively different patterns of growth. The aim of the current article was to explore the impact of latent class separation (i.e., how similar growth trajectories are across latent classes) on GMM performance. Several estimation conditions were compared: maximum likelihood via the expectation maximization (EM) algorithm and the Bayesian framework implementing diffuse priors, “accurate” informative priors, weakly informative priors, data-driven informative priors, priors reflecting partial-knowledge of parameters, and “inaccurate” (but informative) priors. The main goal was to provide insight about the optimal estimation condition under different degrees of latent class separation for GMM. Results indicated that optimal parameter recovery was obtained though the Bayesian approach using “accurate” informative priors, and partial-knowledge priors showed promise for the recovery of the growth trajectory parameters. Maximum likelihood and the remaining Bayesian estimation conditions yielded poor parameter recovery for the latent class proportions and the growth trajectories.
Journal
Psychological Methods – American Psychological Association
Published: Jun 25, 2013