Power of Models in Longitudinal Study: Findings From a Full-Crossed Simulation Design

Hua Fang, University of Massachusetts Medical School
Gordon P. Brooks, Ohio University
Maria L. Rizzo, Bowling Green State University
Kimberly Andrews Espy, University of Nebraska-Lincoln
Robert S. Barcikowski, Ohio University

Abstract

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3 variance-covariance structures. The results from a full-crossed simulation design suggest that traditional repeated measures have significantly higher power than do hierarchical multivariate linear models for main effects, but they have significantly lower power for interaction effects in most situations. Significant power differences are also exhibited when power is compared across different covariance structures.