A Monte Carlo Power Analysis of Traditional Repeated Measures and Hierarchical Multivariate Linear Models in Longitudinal Data Analysis

Authors

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

Date of this Version

5-2008

Citation

Journal of Modern Applied Statistical Methods, May, 2008, Vol. 7, No. 1, 101-119

Comments

Copyright © 2008 JMASM, Inc.

Abstract

The power properties of traditional repeated measures and hierarchical linear models have not been clearly determined in the balanced design for longitudinal studies in the current literature. A Monte Carlo power analysis of traditional repeated measures and hierarchical multivariate linear models are presented under three variance-covariance structures. Results suggest that traditional repeated measures have higher power than hierarchical linear models for main effects, but lower power for interaction effects. Significant power differences are also exhibited when power is compared across different covariance structures. Results also supplement more comprehensive empirical indexes for estimating model precision via bootstrap estimates and the approximate power for both main effects and interaction tests under standard model assumptions.