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Standardized Mean Differences: Not So Standard After All.

Juyoung Jung1, Ariel M Aloe1

  • 1Educational Measurement and Statistics University of Iowa Iowa City Iowa USA.

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces harmonized standardized mean differences (HSMDs) to address distortions in meta-analyses caused by sample variability. HSMDs offer a novel sensitivity analysis for more reliable effect size estimation and robust meta-analytic conclusions.

Keywords:
coefficient of variationdata harmonizationeffect sizesmeta‐analysisstandardized mean differences

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Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Standardized mean differences (SMDs), like Cohen's d and Hedges' g, are common in meta-analyses but are sensitive to within-study variability.
  • This sensitivity can distort individual effect size estimates and impact overall meta-analytic findings.

Purpose of the Study:

  • Introduce harmonized standardized mean differences (HSMDs) as a novel sensitivity analysis framework.
  • Evaluate and address distortions in meta-analysis effect sizes caused by within-study sample variability.
  • Enhance the comprehensiveness of meta-analytic synthesis.

Main Methods:

  • Harmonize relative within-study variability across studies using the coefficient of variation (CV) to set empirical benchmarks.
  • Recalculate SMDs under consistent variability assumptions.
  • Apply the HSMD framework to meta-analytic data to assess the influence of study-specific standard deviations.

    • Main Results:

    • Demonstrate the extent to which original effect sizes and pooled results are influenced by initial standardization variability.
    • Quantify the impact of within-study variability on meta-analytic outcomes.
    • Showcase the framework's ability to incorporate studies lacking reported variability metrics.

    Conclusions:

    • HSMDs provide a robust method for evaluating sensitivity to within-study variability in meta-analyses.
    • This novel framework improves the reliability of effect size estimation and meta-analytic conclusions.
    • The HSMD approach enhances meta-analytic synthesis by accommodating diverse study data.