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This summary is machine-generated.

This study introduces a corrective term to accurately estimate effect sizes, specifically Cohen's d, when dealing with aggregated data from multiple trials. The new method provides a more precise range for effect size estimation in research and meta-analyses.

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Area of Science:

  • Psychological research methods
  • Statistical analysis in behavioral sciences

Background:

  • Cohen's d is frequently overestimated when data from multiple trials are aggregated.
  • Previous research by Brand et al. (2011) highlighted this issue but offered limited solutions.

Purpose of the Study:

  • To propose a corrective term for Cohen's d to address overestimation in aggregated trial data.
  • To provide a more accurate effect size estimation method for studies involving multiple trials.

Main Methods:

  • Developed a corrective term, denoted as d'c, incorporating the number and correlation of trials.
  • Conducted a simulation study to validate the precision of the proposed d'c.

Main Results:

  • The simulation results demonstrated that the corrective term d'c yields a more precise estimation of trial-level effects.
  • The proposed method offers improved accuracy compared to the original Cohen's d calculation for aggregated data.

Conclusions:

  • The corrective term d'c, combined with estimates of inter-trial correlation, offers a more precise effect size range than previously suggested.
  • This approach is recommended for practical application in studies and meta-analyses with multi-trial data structures.