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Meta-evaluation of meta-analysis: ten appraisal questions for biologists
Open accessAbstract
Meta-analysis is a statistical procedure for analyzing the combined data from different studies, and can be a major source of concise up-to-date information. The overall conclusions of a meta-analysis, however, depend heavily on the quality of the meta-analytic process, and an appropriate evaluation of the quality of meta-analysis (meta-evaluation) can be challenging. We outline ten questions biologists can ask to critically appraise a meta-analysis. These questions could also act as simple and accessible guidelines for the authors of meta-analyses. We focus on meta-analyses using non-human species, which we term 'biological' meta-analysis. Our ten questions are aimed at enabling a biologist to evaluate whether a biological meta-analysis embodies 'mega-enlightenment', a 'mega-mistake', or something in between.
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“This bias, known as publication bias, can be mitigated by gathering the most comprehensive database possible (Lortie et al., 2007; Nakagawa et al., 2017).”
From Acoustic indices as proxies for biodiversity: a meta‐analysis (2022) · cites this paper as Nakagawa et al., 2017
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“since poorly and inexpertly conducted meta-analyses can provide biased and misleading results (24, 32).”
From Agricultural diversification promotes multiple ecosystem services without compromising yield (2020) · cites this paper as [32]
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“While the number of published meta-analyses has continued to increase rapidly within and among disciplines, too many meta-analyses are of low quality 13-15 .”
From Meta-analysis and the science of research synthesis (2018) · cites this paper as [15]
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Metadata
- Authors
- Nakagawa, S., Noble, D. W. A., Senior, A. M., & Lagisz, M.
- Affiliations
- Garvan Institute of Medical Research (AU)
- UNSW Sydney (AU)
- The University of Sydney (AU)
- Corresponding author
- Nakagawa, Shinichi
- Venue
- BMC Biology
- Published
- 2017-03-03
- Volume
- 15
- Issue
- 1
- Pages
- 18-18
- ISSN
- 1741-7007 (ISSN-L)
- PMC
- PMC5336618
- OpenAlex
- W2593372779
- Cited by
- 579
- References
- 111
- Citations by year
- 2026: 84 · 2025: 74 · 2024: 71 · 2023: 66 · 2022: 77 · 2021: 50 · 2020: 64 · 2019: 44 · 2018: 37 · 2017: 12
- FWCI
- 10.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- Funders
- University of New South Wales
- University of Sydney
- Australian Research Council (DE150101774, FT130100268)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Keywords
- meta-evaluation, meta-analysis quality, critical appraisal, non-human species
- MeSH terms
- Documentation, Humans, Models, Theoretical, Publications, Meta-Analysis as Topic, Publication Bias, Knowledge
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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