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Correcting for Bias in Psychology: A Comparison of Meta-Analytic Methods
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Abstract
Publication bias and questionable research practices in primary research can lead to badly overestimated effects in meta-analysis. Methodologists have proposed a variety of statistical approaches to correct for such overestimation. However, it is not clear which methods work best for data typically seen in psychology. Here, we present a comprehensive simulation study in which we examined how some of the most promising meta-analytic methods perform on data that might realistically be produced by research in psychology. We simulated several levels of questionable research practices, publication bias, and heterogeneity, and used study sample sizes empirically derived from the literature. Our results clearly indicated that no single meta-analytic method consistently outperformed all the others. Therefore, we recommend that meta-analysts in psychology focus on sensitivity analyses—that is, report on a variety of methods, consider the conditions under which these methods fail (as indicated by simulation studies such as ours), and then report how conclusions might change depending on which conditions are most plausible. Moreover, given the dependence of meta-analytic methods on untestable assumptions, we strongly recommend that researchers in psychology continue their efforts to improve the primary literature and conduct large-scale, preregistered replications. We provide detailed results and simulation code at https://osf.io/rf3ys and interactive figures at http://www.shinyapps.org/apps/metaExplorer/ .
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In the citing paper
“The absence of publication bias is highly unlikely unless a set of preregistered reports is examined (Carter et al. 2019).”
From The gamification of learning: a meta-analysis (2020) · cites this paper as Carter et al. 2019
In the citing paper
“The trim-and-fill method (Duval & Tweedie, 2000) tends to adjust too little when bias is strong (Carter et al., 2019), and Fail-Safe N (Rosenthal, 1979) neither tests nor adjusts for the presence of bias.”
From Do Salient Social Norms Moderate Mortality Salience Effects? A (Challenging) Meta-Analysis of Terror Management Studies (2023) · cites this paper as Carter et al., 2019
In the citing paper
“Bias adjustments can improve performance relative to a meta-analysis that does not adjust for publication bias (Carter et al., 2019; McShane et al., 2016; Moreno et al., 2009; Simonsohn et al., 2014).”
From Do Salient Social Norms Moderate Mortality Salience Effects? A (Challenging) Meta-Analysis of Terror Management Studies (2023) · cites this paper as Carter et al., 2019
In the citing paper
“negative and statistically significant SPET estimates have been observed in both simulation studies (Carter et al., 2019) and meta-analysis (Hilgard et al., 2019).”
From Do Salient Social Norms Moderate Mortality Salience Effects? A (Challenging) Meta-Analysis of Terror Management Studies (2023) · cites this paper as Carter et al., 2019
In the citing paper
“On the one hand, this may be an underestimate of average study power—in simulation studies, p-hacking adds large p values to the p-curve, canceling out the right skew and biasing the p-curve estimate downward (Carter et al., 2019).”
From Do Salient Social Norms Moderate Mortality Salience Effects? A (Challenging) Meta-Analysis of Terror Management Studies (2023) · cites this paper as Carter et al., 2019
In the citing paper
“SPET and SPEESE have a downward bias in the presence of p-hacking (Carter et al., 2019)”
From Do Salient Social Norms Moderate Mortality Salience Effects? A (Challenging) Meta-Analysis of Terror Management Studies (2023) · cites this paper as Carter et al., 2019
In the citing paper
“There may also be downward bias inflicted by p-hacking (Carter et al., 2019).”
From Do Salient Social Norms Moderate Mortality Salience Effects? A (Challenging) Meta-Analysis of Terror Management Studies (2023) · cites this paper as Carter et al., 2019
In the citing paper
“Further, PET-PEESE can underestimate the effect size when there is publication bias and when p-hacking is present (Carter et al., 2019), which are two conditions likely affecting the literature.”
From Managing the terror of publication bias: A comprehensive p-curve analysis of the Terror Management Theory literature (2023) · cites this paper as Carter et al., 2019
In the citing paper
“However, this counterintuitive finding may be explained by the fact that PET is prone to producing imprecise and biased estimates under high heterogeneity, small sample sizes, and a large proportion of p-hacking (Stanley & Doucouliagos, 2014; Stanley 2017; Carter et al., 2019).”
From Managing the terror of publication bias: A comprehensive p-curve analysis of the Terror Management Theory literature (2023) · cites this paper as Carter et al., 2019
In the citing paper
“Simulation studies by Carter et al. (2019) showed that WAAP-WLS can provide a reasonably unbiased estimate of the effect size with a high power to detect the effect given that a true underlying effect exists.”
From Managing the terror of publication bias: A comprehensive p-curve analysis of the Terror Management Theory literature (2023) · cites this paper as Carter et al. (2019)
In the citing paper
“This could lead selection model to underestimate effects in the presence of publication bias and p-hacking, as shown in simulation studies (Carter et al., 2019).”
From Managing the terror of publication bias: A comprehensive p-curve analysis of the Terror Management Theory literature (2023) · cites this paper as Carter et al., 2019
In the citing paper
“Lastly, PET-PEESE can underestimate the size of small effects in the presence of p-hacking and publication bias (Carter et al., 2019).”
From Managing the terror of publication bias: A comprehensive p-curve analysis of the Terror Management Theory literature (2023) · cites this paper as Carter et al., 2019
In the citing paper
“However, when effects are highly heterogeneous and when sample sizes are small, the PET-PEESE analysis is known to yield imprecise estimates (Stanley & Doucouliagos, 2014; Stanley, 2017; Carter et al., 2019).”
From Managing the terror of publication bias: A comprehensive p-curve analysis of the Terror Management Theory literature (2023) · cites this paper as Carter et al., 2019
In the citing paper
“there is a danger that uncritical application can lead to inappropriate conclusions being drawn 68,73,74”
From Investigating and dealing with publication bias and other reporting biases in meta-analyses of health research: a review (2021) · cites this paper as [74]
In the citing paper
“the available evidence suggests that no single method outperforms others in all scenarios”
From Investigating and dealing with publication bias and other reporting biases in meta-analyses of health research: a review (2021) · cites this paper as [74]
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Metadata
- Authors
- Carter, E. C., Schönbrodt, F. D., Gervais, W. M., & Hilgard, J.
- Affiliations
- DEVCOM Army Research Laboratory (US)
- Ludwig-Maximilians-Universität München (DE)
- University of Kentucky (US)
- Illinois State University (US)
- Corresponding author
- Carter, Evan C.
- Venue
- Advances in Methods and Practices in Psychological Science
- Publisher
- SAGE Publications
- Published
- 2019-06
- Volume
- 2
- Issue
- 2
- Pages
- 115-144
- ISSN
- 2515-2459 (ISSN-L)
- 2515-2467
- OpenAlex
- W2781957993
- Cited by
- 588
- References
- 76
- Citations by year
- 2026: 43 · 2025: 60 · 2024: 69 · 2023: 86 · 2022: 83 · 2021: 87 · 2020: 79 · 2019: 54
- FWCI
- 50.69
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Advances in Methods and Practices in Psychological Science (publishedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (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
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- publication bias, questionable research practices, effect size overestimation, meta-analytic methods, sensitivity analysis, heterogeneity, simulation study, pre-registered replication
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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