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

Authors

Hua Fang, University of Nebraska-Lincoln
Gordon P. Brooks, Ohio UniversityFollow
Maria L. Rizzo, Bowling Green State UniversityFollow
Kimberly A. Espy, University of Nebraska-LincolnFollow
Robert S. Barcikowski, Ohio University

Date of this Version

2009

Comments

Published in final edited form as: J Exp Educ. 2009 April 1; 77(3): 215–254. doi:10.3200/JEXE.77.3.215-254.

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.