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What can we learn from 1,000 meta-analyses across 10 different disciplines?
Weilun Wu1, Jianhua Duan2, W Robert Reed1
1Department of Economics and Finance and UCMeta, University of Canterbury, Christchurch, New Zealand.
Research Synthesis Methods
|February 2, 2026
Summary
This study of 1,000 meta-analyses reveals significant variations in methodological practices across disciplines. Key differences include effect size usage, publication bias testing, and handling of dependent data, highlighting areas for improved research synthesis.
Area of Science:
- Cross-disciplinary research synthesis
- Methodological practices in quantitative research
Background:
- Meta-analysis is a crucial tool for synthesizing research findings across various scientific disciplines.
- Understanding current methodological practices is essential for improving the reliability and validity of meta-analytic results.
Purpose of the Study:
- To document and compare methodological practices in 1,000 meta-analyses from 10 diverse disciplines.
- To identify variations in meta-analytic techniques, including data handling, bias assessment, and advanced statistical methods.
Main Methods:
- Analysis of 1,000 meta-analyses across medicine, psychology, education, biology, and economics.
- Examination of meta-analysis size, effect size characteristics, use of unpublished studies, and publication bias adjustments.
Main Results:
- Significant interdisciplinary differences observed in meta-analysis size, effect size types, and use of unpublished data.
- Limited application of advanced statistical methods (e.g., multilevel models) to handle dependent effect sizes.
- Inconsistent or inadequate handling of correlations as effect sizes and underutilization of meta-regression.
Conclusions:
- Methodological practices in meta-analysis vary substantially across disciplines, impacting research synthesis quality.
- There is a need for greater adoption of advanced statistical techniques and consistent reporting standards in meta-analysis.
- This study provides a benchmark for improving meta-analytic rigor and serves as a guide for researchers.
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