Cited by 220Meta-analysis and systematic reviewsPsychology Research and Bibliometricsscientometrics and bibliometrics research

Conducting Meta-Analyses Based on p Values: Reservations and Recommendations for Applying p -Uniform and p -Curve

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van Aert, Robbie C. M., Wicherts, Jelte M., van Assen, Marcel A. L. M.·2016·Perspectives on Psychological Science

Abstract

Because of overwhelming evidence of publication bias in psychology, techniques to correct meta-analytic estimates for such bias are greatly needed. The methodology on which the p-uniform and p-curve methods are based has great promise for providing accurate meta-analytic estimates in the presence of publication bias. However, in this article, we show that in some situations, p-curve behaves erratically, whereas p-uniform may yield implausible estimates of negative effect size. Moreover, we show that (and explain why) p-curve and p-uniform result in overestimation of effect size under moderate-to-large heterogeneity and may yield unpredictable bias when researchers employ p-hacking. We offer hands-on recommendations on applying and interpreting results of meta-analyses in general and p-uniform and p-curve in particular. Both methods as well as traditional methods are applied to a meta-analysis on the effect of weight on judgments of importance. We offer guidance for applying p-uniform or p-curve using R and a user-friendly web application for applying p-uniform.

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  • With moderate heterogeneity (I2 = 42.01%), the true effect size estimates are expected to be robust.

    “With a moderate ( < 50%) level of heterogeneity in included comparisons ( I 2 = 42.01%), we expect these estimates to be robust (van Aert etal., 2016).”

    From When Problem Solving Followed by Instruction Works: Evidence for Productive Failure (2021) · cites this paper as van Aert et al., 2016

    In the section “If Heterogeneity Is Moderate to Large, p-Curve and p-Uniform Overestimate Effect Size” (page unavailable), the paper reports that effect-size estimates are biased at its moderate-heterogeneity condition (I² ≈ 0.5). It does not assess I² = 42.01%, so that exact threshold is not tested; its finding does not support the cited expectation that moderate heterogeneity makes the estimates robust. (LLM, conf=0.84)

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Research works printed but not linked: 2

  1. American Psychological Association. (2010). Publication manual (6th ed.). Washington, DC: Author.
  2. Augusteijn H. E. M. (2015). The effect of publication bias on the Q test and assessment of heterogeneity (Unpublished master’s thesis). Tilburg University, Tilburg, the Netherlands.

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Authors
van Aert, R. C. M., Wicherts, J. M., & van Assen, M. A. L. M.
Affiliations
  • Department of Methodology and Statistics, Tilburg University
  • Tilburg University (NL)
Venue
Perspectives on Psychological Science
Published
2016-09
Volume
11
Issue
5
Pages
713-729
ISSN
  • 1745-6916 (ISSN-L)
  • 1745-6924
OpenAlex
W2526603989
Cited by
219
References
54
Citations by year
2026: 12 · 2025: 13 · 2024: 24 · 2023: 22 · 2022: 28 · 2021: 22 · 2020: 25 · 2019: 23 · 2018: 20 · 2017: 27 · 2016: 3
FWCI
22.73
Type
journal article
Language
en
Open access
Locations
  • Perspectives on Psychological Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • Research portal (Tilburg University) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • OSF Preprints (OSF Preprints) (submittedVersion)
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
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
Keywords
p-curve, publication bias, meta-analysis, effect size estimation, p-hacking, heterogeneity, effect size overestimation, R package, web application
MeSH terms
Data Interpretation, Statistical, Humans, Psychology, Software, Meta-Analysis as Topic, Models, Statistical, Publication Bias, Internet

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