Statistical Power of the Multiple Domain Latent Growth Model for Detecting Group Differences. Issue 5 (3rd September 2018)

• Record Type: Journal Article
• Authors: Lee, Kejin; Whittaker, Tiffany A.
• Is Part Of: Structural equation modeling. Volume 25:Issue 5(2018)
• Page Start: 700
• Page End: 714
• Publication Date: 2018-09-03
• Subjects: latent growth modeling -- Monte Carlo study -- multiple domain latent growth model -- statistical power; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1426990
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : The latent growth model (LGM) in structural equation modeling (SEM) may be extended to allow for the modeling of associations among multiple latent growth trajectories, resulting in a multiple domain latent growth model (MDLGM). While the MDLGM is conceived as a more powerful multivariate analysis technique, the examination of its methodological performance is very limited. Hence, the present study compared the power of the MDLGM with that of a set of univariate LGMs for detecting group differences in growth rates over time using a Monte Carlo study with a two-group and two-domain design. The results indicated that there were different scenarios where the power rates for the MDLGM were greater than that of the set of LGMs (and vice versa) due to a joint function of the two domains' intercorrelation size and the group difference effect size.