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Bias correction for Cohen's d.

Xiaofeng Steven Liu1

  • 1Department of Educational Studies, University of South Carolina, Columbia, SC, USA.

The Journal of General Psychology
|February 16, 2023
PubMed
Summary
This summary is machine-generated.

Bias in Cohen's d, a common effect size, can be corrected using non-parametric bootstrapping. This method is effective for small studies, offering a robust alternative to traditional bias correction techniques.

Keywords:
BiasCohen’s dEffect sizebootstrap

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Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Cohen's d is a widely used effect size measure.
  • The traditional calculation of Cohen's d is known to have a positive bias.
  • This bias is particularly problematic in small studies with limited sample sizes.

Purpose of the Study:

  • To introduce a bias correction method for Cohen's d.
  • To address the limitations of traditional bias correction methods, especially for small datasets.
  • To demonstrate the efficacy of non-parametric bootstrapping for bias estimation and removal in Cohen's d.

Main Methods:

  • Non-parametric bootstrapping was employed to estimate and correct bias in Cohen's d.
  • The bootstrapping approach does not rely on strict distributional assumptions.
  • A real-world dataset was used to illustrate the practical application of the method.

Main Results:

  • The study successfully implemented bootstrap bias estimation for Cohen's d.
  • Sizable bias in Cohen's d was effectively removed using the proposed bootstrapping technique.
  • The non-parametric approach proved advantageous where traditional methods may fail.

Conclusions:

  • Non-parametric bootstrapping provides a reliable and flexible method for correcting bias in Cohen's d.
  • This technique is especially valuable for researchers working with small sample sizes.
  • Accurate effect size estimation is crucial for robust scientific interpretation.