Modeling Publication Selection Effects in Meta-Analysis
Open accessAbstract
Publication selection effects arise in meta-analysis when the effect magnitude estimates are observed in (available from) only a subset of the studies that were actually conducted and the probability that an estimate is observed is related to the size of that estimate. Such selection effects can lead to substantial bias in estimates of effect magnitude. Research on the selection process suggests that much of the selection occurs because researchers, reviewers and editors view the results of studies as more conclusive when they are more highly statistically significant. This suggests a model of the selection process that depends on effect magnitude via the p-value or significance level. A model of the selection process involving a step function relating the p-value to the probability of selection is introduced in the context of a random effects model for meta-analysis. The model permits estimation of a weight function representing selection along the mean and variance of effects. Some ideas for graphical procedures and a test for publication selection are also introduced. The method is then applied to a meta-analysis of test validity studies.
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Connections between sample sizes and effect sizes would violate the assumptions needed to interpret asymmetric funnel plots and Egger's test as publication bias.
“Such connections between sample sizes and effect sizes would violate the assumptions needed to interpret asymmetric funnel plots and Egger's test as publication bias (it would also violate the assumptions underlying the selection models suggested by, e.g., Hedges, 1992 and Andrews & Kasy, 2019).”
From Targeted school‐based interventions for improving reading and mathematics for students with or at risk of academic difficulties in Grades K‐6: A systematic review (2021) · cites this paper as Hedges, 1992
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Publication bias as a function of study characteristics.
Coburn, K. M., & Vevea, J. L. (2015) · Psychological Methods · doi:10.1037/met0000046 · Cited by 107 · see citing works
Estimating the Value of a Statistical Life: The Importance of Omitted Variables and Publication Bias
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Publication Bias in Recent Meta-Analyses
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Asymmetry as a predictor of growth, fecundity and survival
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Z-curve 2.0: Estimating Replication Rates and Discovery Rates
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Practical estimates of the effect of publication bias in meta- analysis
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- Authors
- Hedges, L. V.
- Corresponding author
- Hedges, Larry V.
- ORCID
- Hedges, Larry V.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 1992-05-01
- Volume
- 7
- Issue
- 2
- Pages
- 246-255
- ISSN
- 0883-4237 (ISSN-L)
- 2168-8745
- OpenAlex
- W2087875612
- Cited by
- 359
- References
- 34
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 16 · 2023: 10 · 2022: 16 · 2021: 11 · 2020: 39 · 2019: 17 · 2018: 14 · 2017: 9 · 2016: 11 · 2015: 13 · 2014: 8 · 2013: 5 · 2012: 11
- FWCI
- 10.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Statistical Science (publishedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Keywords
- publication bias, meta-analysis, p-values, significance level, selection probability, random effects models, weight functions, test validity, graphical procedure
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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