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

Citation data:

Journal of Modern Applied Statistical Methods, ISSN: 1538-9472, Vol: 7, Issue: 1, Page: 101-119

Publication Year:
2008
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Repository URL:
https://digitalcommons.wayne.edu/jmasm/vol7/iss1/9
DOI:
10.22237/jmasm/1209614880
Author(s):
Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly A.; Barcikowski, Robert S.
Publisher(s):
Wayne State University Library System
Tags:
Mathematics; Decision Sciences; Monte Carlo; power analysis; traditional repeated measures; hierarchical multivariate linear models; longitudinal study; Applied Statistics; Social and Behavioral Sciences; Statistical Theory
article description
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. Copyright © 2008 JMASM, Inc.