A Monte Carlo power analysis of traditional repeated measures and hierarchical multivariate linear models in longitudinal data analysis

Hua Fang, Gordon P. Brooks, Maria L. Rizzo, Kimberly A. Espy, Robert S. Barcikowski

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)101-119
Number of pages19
JournalJournal of Modern Applied Statistical Methods
Volume7
Issue number1
DOIs
StatePublished - May 2008

Keywords

  • Hierarchical multivariate linear models
  • Longitudinal study
  • Monte Carlo
  • Power analysis
  • Traditional repeated measures

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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