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How many repeated measurements are useful?

J E Overall1

  • 1University of Texas Health Science Center, USA.

Journal of Clinical Psychology
|May 1, 1996
PubMed
Summary

Increasing repeated measurements in studies generally reduces statistical power, especially with correlated data. This finding impacts the design of repeated measures studies for optimal significance testing.

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Area of Science:

  • Biostatistics
  • Experimental Design
  • Statistical Power Analysis

Background:

  • Repeated measurements designs are common in scientific research.
  • The number of measurements can influence statistical power.
  • Serial dependencies and heterogeneous correlations can affect analysis outcomes.

Purpose of the Study:

  • To investigate the impact of varying the number of repeated measurements on statistical power.
  • To evaluate the effectiveness of repeated measures ANOVA in split-plot designs.
  • To assess the influence of baseline measurements as a covariate.

Main Methods:

  • Simulated data for a two-group repeated measurements design.
  • Analysis using repeated measures ANOVA with Geisser-Greenhouse correction.
  • Comparison of analyses with and without baseline measurements as a covariate.
  • Monte Carlo simulations to assess power.

Main Results:

  • Increasing the number of measurements generally had negative or neutral effects on statistical power.
  • Serial dependencies and heterogeneous correlations exacerbated power issues.
  • The inclusion of baseline measurements as a covariate did not consistently improve power.

Conclusions:

  • Optimizing the number of repeated measurements is crucial for study design.
  • Researchers should carefully consider potential serial dependencies and correlations.
  • Standard statistical methods may require adjustments in complex repeated measures scenarios.

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