Growth mixture models combine latent growth curve models and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. Analyses based on these models are becoming quite common in social and behavioral science research because of recent advances in computing, the availability of specialized statistical programs, and the ease of programming. In this article, we show how mixture models can be fit to examine the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The approach is illustrated using empirical longitudinal data along with an easy to use computerized implementation.
|Original language||English (US)|
|Number of pages||12|
|Journal||Structural Equation Modeling|
|State||Published - May 4 2019|
Bibliographical notePublisher Copyright:
© 2018, Copyright © 2019 Taylor & Francis Group, LLC.
- growth mixture modeling
- individual case residuals
- latent growth curve models
- unobserved heterogeneity