title: A Monte Carlo Power Analysis of Traditional Repeated Measures and Hierarchical Multivariate Linear Models in Longitudinal Data Analysis.
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.
authors.affiliation: University of Nebraska-Lincoln jfang2@unlnotes.unl.edu .
authors.forename: Hua
authors.initials: H
authors.lastname: Fang
authors.affiliation: Ohio University brooksg@ohio.edu .
authors.forename: Gordon P
authors.initials: GP
authors.lastname: Brooks
authors.affiliation: Bowling Green State University mrizzo@bgnet.bgsu.edu .
authors.forename: Maria L
authors.initials: ML
authors.lastname: Rizzo
authors.affiliation: University of Nebraska-Lincoln.
authors.forename: Kimberly A
authors.initials: KA
authors.lastname: Espy
authors.affiliation: Ohio University. barcikow@ohio.edu.
authors.forename: Robert S
authors.initials: RS
authors.lastname: Barcikowski
keywords: Monte Carlo; hierarchical multivariate linear models; longitudinal study; power analysis; traditional repeated measures