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 Andrews Espy, Robert S. Barcikowski

Research output: Contribution to journalArticle

2 Citations (Scopus)

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
StatePublished - May 2008
Externally publishedYes

Fingerprint

Multivariate Linear Model
Longitudinal Data Analysis
Repeated Measures
Power Analysis
Hierarchical Linear Models
Main Effect
Covariance Structure
Balanced Design
Interaction Effects
Longitudinal Study
High Power
Bootstrap
Standard Model
Interaction
Estimate
Repeated measures
Longitudinal data analysis
Model

Keywords

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

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Cite this

A Monte Carlo power analysis of traditional repeated measures and hierarchical multivariate linear models in longitudinal data analysis. / Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S.

In: Journal of Modern Applied Statistical Methods, Vol. 7, No. 1, 05.2008, p. 101-119.

Research output: Contribution to journalArticle

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