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Effect sizes for paired data in sports and exercise science should use change score variability, not pre-test variability. This ensures effect size calculations accurately reflect intervention magnitude and align with statistical test results.

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Area of Science:

  • Sports and Exercise Science
  • Biostatistics
  • Research Methodology

Background:

  • Effect sizes are crucial for quantifying the magnitude of research findings, independent of sample size.
  • Accurate effect size calculation is essential for correct interpretation of statistical significance.
  • Previous research in sports and exercise science has often incorrectly calculated effect sizes for paired data.

Purpose of the Study:

  • To illustrate the correct method for calculating effect sizes with paired data in sports and exercise science.
  • To highlight the importance of using change score variability instead of pre-test variability.
  • To ensure effect size calculations align with the underlying statistical test principles.

Main Methods:

  • Statistical analysis and explanation of effect size calculations.
  • Illustrative examples using sports and exercise science data.
  • Comparison of effect size calculations using pre-test variability versus change score variability.

Main Results:

  • Effect sizes calculated using pre-test variability do not accurately represent the intervention's magnitude.
  • Correct effect size calculation for paired data requires using the standard deviation (SD) of the change score.
  • Using the change score SD ensures the effect size reflects the variability of the intervention, aligning with the test statistic.

Conclusions:

  • Effect size calculations for paired data must be based on the standard deviation of the change score.
  • This method provides a more accurate and meaningful measure of effect magnitude in sports and exercise research.
  • Correcting effect size calculations enhances the interpretability and comparability of research findings.