Statistical Power of the Multiple-Domain Latent Growth Model for Detecting Group Differences

• In Event: 52.076 - Longitudinal Data Modeling

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 LGMs for detecting group differences in growth rates over time using a two-group and two-domain design. The results of this study indicated that the MDLGM was more powerful and resulted in lower Type Ι error rates than a set of LGMs.

Authors

• Kejin Lee, The University of Texas - Austin

• Tiffany Ann Whittaker, The University of Texas - Austin