Fitting Structural Equation Model Trees and Latent Growth Curve Mixture Models in Longitudinal Designs: The Influence of Model Misspecification. Issue 4 (4th July 2017)

• Record Type: Journal Article
• Authors: Usami, Satoshi; Hayes, Timothy; McArdle, John
• Is Part Of: Structural equation modeling. Volume 24:Issue 4(2017)
• Page Start: 585
• Page End: 598
• Publication Date: 2017-07-04
• Subjects: classification -- decision trees -- latent growth curve mixture models -- longitudinal data -- structural equation model trees; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2016.1266267
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : When conducting longitudinal research, the investigation of between-individual differences in patterns of within-individual change can provide important insights. In this article, we use simulation methods to investigate the performance of a model-based exploratory data mining technique—structural equation model trees (SEM trees; Brandmaier, Oertzen, McArdle, & Lindenberger, 2013)—as a tool for detecting population heterogeneity. We use a latent-change score model as a data generation model and manipulate the precision of the information provided by a covariate about the true latent profile as well as other factors, including sample size, under the possible influences of model misspecifications. Simulation results show that, compared with latent growth curve mixture models, SEM trees might be very sensitive to model misspecification in estimating the number of classes. This can be attributed to the lower statistical power in identifying classes, resulting from smaller differences of parameters prescribed by the template model between classes.