Power of Latent Growth Modeling for Detecting Group Differences in Linear Growth Trajectory Parameters

Abstract

This simulation study focused on the power for detecting group differences in linear growth trajectory parameters within the framework of structural equation modeling (SEM) and compared the latent growth modeling (LGM) approach to the more traditional repeated-measures analysis of variance (ANOVA) approach. Several patterns of group differences in linear growth trajectories were considered. SEM growth modeling consistently showed higher statistical power for detecting group differences in the linear growth slope than repeated-measures ANOVA. For small group differences in the growth trajectories, large sample size (e.g., N > 500) would be required for adequate statistical power. For medium or large group differences, moderate or small sample size would be sufficient for adequate power. Some future research directions are discussed.