Journal
STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
Volume 26, Issue 6, Pages 967-985Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/10705511.2019.1590146
Keywords
auxiliary; distal outcome; finite mixture; latent class; stepwise
Funding
- Institute of Education Sciences [R305A160157]
- Institute of Education Sciences, U.S. Department of Education [R305A160157]
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Including auxiliary variables such as antecedent and consequent variables in mixture models provides valuable insight in understanding the population heterogeneity embodied by a latent class variable. The model building process regarding how to include predictors/correlates and outcomes of the latent class variables into mixture models is an area of active research. As such, new methods of including these variables continue to emerge and best practices for the application of these methods in real data settings (including simple guidelines for choosing amongst them) are still not well established. This paper focuses on one type of auxiliary variable-distal outcomes-providing an overview of the methods currently available for estimating the effects of latent class membership on subsequent distal outcomes. We illustrate the recommended methods in the software packages Mplus and Latent Gold using a latent class model to capture population heterogeneity in students' mathematics attitudes, linking latent class membership to two distal outcomes.
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