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Related Experiment Videos

Single-factor repeated-measures designs: analysis and interpretation.

Jeffrey A Gliner1, George A Morgan, Robert J Harmon

  • 1Colorado State University, Fort Collins, USA.

Journal of the American Academy of Child and Adolescent Psychiatry
|August 7, 2002
PubMed
Summary

This column explains selecting statistical tests for repeated-measures designs, including t-tests and ANOVA. It emphasizes that statistical significance doesn't always mean a large effect size, especially with large samples.

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Area of Science:

  • Statistics
  • Psychology
  • Research Methodology

Background:

  • Discusses appropriate statistical tests for single-factor within-subjects/repeated-measures designs.
  • Highlights parametric tests like paired t-tests and repeated-measures ANOVA.
  • Mentions less common nonparametric alternatives for these designs.

Discussion:

  • Explains the selection and interpretation of statistical tests.
  • Addresses the importance of effect size measures, even when not initially provided.
  • Illustrates concepts with an example from Compton et al. (2001).

Key Insights:

  • Statistical significance (p < .001) does not automatically imply a large effect size.
  • Effect sizes can be calculated from means and standard deviations.

Related Experiment Videos

  • A small sample size (N=14) in the Compton example indicated a large effect size.

    • Outlook:

    • Recommends careful consideration of statistical tests and effect sizes in research.
    • Encourages researchers to compute and report effect sizes for robust findings.
    • Suggests further exploration of nonparametric tests in specific research contexts.