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Recommended effect size statistics for repeated measures designs.

Roger Bakeman1

  • 1Department of Psychology, Georgia State University, Atlanta, Georgia 30302-5010, USA. bakeman@gsu.edu

Behavior Research Methods
|January 13, 2006
PubMed
Summary
This summary is machine-generated.

Researchers should report generalized eta squared (eta2G) for repeated measures ANOVA. This effect size statistic offers better comparability across study designs than older measures.

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Area of Science:

  • Statistics
  • Psychology
  • Research Methods

Background:

  • Effect size statistics are crucial for interpreting research findings.
  • Previous methods for calculating effect sizes in repeated measures ANOVA have limitations.
  • Generalized eta squared (eta2G) has been proposed as a more suitable statistic.

Purpose of the Study:

  • To introduce and explain the generalized eta squared (eta2G) effect size statistic.
  • To advocate for the routine use of eta2G in research reporting.
  • To highlight the advantages of eta2G over eta squared and partial eta squared.

Main Methods:

  • The paper explains the calculation and application of generalized eta squared (eta2G).
  • It demonstrates how eta2G can be computed from standard statistical software outputs.
  • Comparisons are made between eta2G and other effect size measures like eta squared and partial eta squared.

Main Results:

  • Generalized eta squared (eta2G) is a generally useful effect size statistic for analyses of variance with repeated measures.
  • Eta2G offers superior comparability between between-subjects and within-subjects designs.
  • The statistic is readily computable from common statistical packages.

Conclusions:

  • Investigators are encouraged to adopt generalized eta squared (eta2G) for reporting effect sizes in repeated measures designs.
  • Routine use of eta2G will enhance the clarity and comparability of research findings.
  • This statistic addresses the need for a consistent effect size measure across different experimental designs.