Journal article
Standardized Mean Differences: No So Standard After All
Campbell systematic review, Vol.21(3), e70056
09/2025
DOI: 10.1002/cl2.70056
PMCID: PMC12358740
PMID: 40831909
Abstract
Meta-analyses often use standardized mean differences (SMDs), such as Cohen's d and Hedges' g, to compare treatment effects. However, these SMDs are highly sensitive to the within-study sample variability used for their standardization, potentially distorting individual effect size estimates and compromising overall meta-analytic conclusions. This study introduces harmonized standardized mean differences (HSMDs), a novel sensitivity analysis framework designed to systematically evaluate and address such distortions. The HSMD harmonizes relative within-study variability across studies by employing the coefficient of variation (CV) to establish empirical benchmarks (e.g., CV quartiles). SMDs are then recalculated under these consistent variability assumptions. Applying this framework to Meta-analytic data reveals the extent to which (original) effect sizes and pooled results are influenced by initial, study-specific standard deviations to standardize mean differences. Furthermore, the method facilitates the inclusion of studies lacking reported variability metrics into the sensitivity analysis, enhancing the comprehensiveness of the meta-analytic synthesis.
Details
- Title: Subtitle
- Standardized Mean Differences: No So Standard After All
- Creators
- Juyoung Jung - University of IowaAriel M. Aloe - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Campbell systematic review, Vol.21(3), e70056
- DOI
- 10.1002/cl2.70056
- PMID
- 40831909
- PMCID
- PMC12358740
- NLM abbreviation
- Campbell Syst Rev
- ISSN
- 1891-1803
- eISSN
- 1891-1803
- Publisher
- John Wiley and Sons Inc
- Language
- English
- Date published
- 09/2025
- Academic Unit
- Education Administration; Psychological and Quantitative Foundations
- Record Identifier
- 9984948120102771
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