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Evaluating meta-analytic methods to detect selective reporting in the presence of dependent effect sizes.
Open accessPublished version of a preprint
Abstract
Selective reporting of results based on their statistical significance threatens the validity of meta-analytic findings. A variety of techniques for detecting selective reporting, publication bias, or small-study effects are available and are routinely used in research syntheses. Most such techniques are univariate, in that they assume that each study contributes a single, independent effect size estimate to the meta-analysis. In practice, however, studies often contribute multiple, statistically dependent effect size estimates, such as for multiple measures of a common outcome construct. Many methods are available for meta-analyzing dependent effect sizes, but methods for investigating selective reporting while also handling effect size dependencies require further investigation. Using Monte Carlo simulations, we evaluate three available univariate tests for small-study effects or selective reporting, including the trim and fill test, Egger's regression test, and a likelihood ratio test from a three-parameter selection model (3PSM), when dependence is ignored or handled using ad hoc techniques. We also examine two variants of Egger's regression test that incorporate robust variance estimation (RVE) or multilevel meta-analysis (MLMA) to handle dependence. Simulation results demonstrate that ignoring dependence inflates Type I error rates for all univariate tests. Variants of Egger's regression maintain Type I error rates when dependent effect sizes are sampled or handled using RVE or MLMA. The 3PSM likelihood ratio test does not fully control Type I error rates. With the exception of the 3PSM, all methods have limited power to detect selection bias except under strong selection for statistically significant effects. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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“Publication bias was assessed both visually, using funnel plots, and statistically, using a multilevel Egger-type regression test, in which the standard error of the effect size was included as a predictor in a mixed-effects model (39).”
From Carbohydrate ingestion during prolonged exercise and net skeletal muscle glycogen utilization: A meta-analysis. (2026) · cites this paper as [39]
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Дмитриевна, К. Ю., Melnik, Y., Novikova, M., Kosaretsky, S., & Zviagintsev, R. (2025) · International Journal of Educational Development · doi:10.1016/j.ijedudev.2025.103274 · Cited by 1
Dolgorukova, A., Protsenko, E., Isaeva, J., Gagloeva, V., Verbitskaya, E., Berkovich, R., & Sokolov, A. (2023) · European Journal of Neuroscience · doi:10.1111/ejn.16030 · Cited by 2
“For this, we fitted a multi-level meta-regression model with a measure of effect size precision (standard error) as a predictor and used RVE to handle dependence (Rodgers & Pustejovsky, 2020).”
MethodLoenneker, H. D., Huber, J. F., Arndt, E., Liepelt‐Scarfone, I., & Nuerk, H. (2021) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/z57wr · Cited by 1
Correcting for outcome reporting bias in a meta-analysis: A meta-regression approachPreprint
van Aert, R. C. M., & Wicherts, J. M. (2021) · doi:10.31222/osf.io/bn8vd · Cited by 1
Mathur, M. B. (2022) · doi:10.31219/osf.io/u7vcb · Cited by 1
Supplemental Material for Effects of Action Video Game Play on Cognitive Skills: A Meta-Analysis
Bediou, B., Rodgers, M. A., Tipton, E., Mayer, R. E., Green, C. S., & Bavelier, D. (2023) · Technology Mind and Behavior · doi:10.1037/tmb0000102.supp · Cited by 4
Self-citationJin, L., Yu, D., Jin, Z., & Li, H. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/63sjt · Cited by 1
Harris, J. L., Swanson, B., & Petersen, I. T. (2023) · doi:10.31234/osf.io/nbqx7 · Cited by 1
Measuring learning and attention to irrelevant distractors in contextual cueingPreprint
Vadillo, M. A., Hernández‐Gutiérrez, D., & Aivar, M. P. (2024) · doi:10.31234/osf.io/26zbg · Cited by 1
Lund, C., Orkin, K., Witte, M., Walker, J., Davies, T., Haushofer, J., Murray, S. M., Bass, J., Murray, L. K., Tol, W. A., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4868762 · Cited by 1
Pourdanandeh, M., & Selander, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.07.642069 · Cited by 1
metaselection: Meta-Analytic Selection Models for Dependent Effect Sizes
Pustejovsky, J. E., Joshi, M., & Citkowicz, M. (2026) · doi:10.32614/cran.package.metaselection · Cited by 1
Self-citationWu, M., & Ouyang, F. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10211-x
“Finally, small-study and publication bias effects were assessed with several diag - nostics adapted to the dependent structure of the data (Rodgers & Pustejovsky, 2021 ).”
de Roo, M., Veenstra, R., & Kretschmer, T. (2021) · doi:10.31234/osf.io/sfdh3
Dolgorukova, A., Protsenko, E., Isaeva, J., Gagloeva, V., Verbitskaya, E. V., & Sokolov, A. Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.05.498780
“For this, we fitted a multi-level meta-regression model with a measure of effect size precision (standard error) as a predictor and used robust variance estimates to handle dependence (Rodgers and Pustejovsky, 2020).”
Method
Metadata
- Authors
- Rodgers, M. A., & Pustejovsky, J. E.
- Affiliations
- The University of Texas at Austin (US)
- Venue
- Psychological Methods
- Published
- 2021-04
- Volume
- 26
- Issue
- 2
- Pages
- 141-160
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W3043790905
- Cited by
- 492
- References
- 0
- Citations by year
- 2026: 111 · 2025: 111 · 2024: 95 · 2023: 72 · 2022: 61 · 2021: 34
- FWCI
- 26.68
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- MetArXiv (OSF Preprints) (submittedVersion)
- MetArXiv (OSF Preprints) (submittedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- selective reporting, publication bias, small-study effects, dependent effect sizes, robust variance estimation, multilevel meta-analysis, type I error rate
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