Cited by 13Meta-analysis and systematic reviewsPsychology Research and BibliometricsStatistical Methods and Bayesian Inference

Challenges and suggestions for defining replication “success” when effects may be heterogeneous: Comment on Hedges and Schauer (2019)

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Mathur, Maya B., VanderWeele, Tyler J.·2019·Psychological Methods

Abstract

Psychological scientists are now trying to replicate published research from scratch to confirm the findings. In an increasingly widespread replication study design, each of several collaborating sites (such as universities) independently tries to replicate an original study, and the results are synthesized across sites. Hedges and Schauer (2019) proposed statistical analyses for these replication projects; their analyses focus on assessing the extent to which results differ across the replication sites, by testing for heterogeneity among a set of replication studies, while excluding the original study. We agree with their premises regarding the limitations of existing analysis methods and regarding the importance of accounting for heterogeneity among the replications. This objective may be interesting in its own right. However, we argue that by focusing only on whether the replication studies have similar effect sizes to one another, these analyses are not particularly appropriate for assessing whether the replications in fact support the scientific effect under investigation or for assessing the power of multisite replication projects. We reanalyze Hedges and Schauer's (2019) example dataset using alternative metrics of replication success that directly address these objectives. We reach a more optimistic conclusion regarding replication success than they did, illustrating that the alternative metrics can lead to quite different conclusions from those of Hedges and Schauer (2019). (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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  • There is no single method for assessing the success or failure of replication attempts.

    “There is no single method for assessing the success or failure of replication attempts”

    From Investigating the replicability of preclinical cancer biology (2021) · cites this paper as Mathur and VanderWeele, 2019

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References 9 · Cited by 13

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  1. Crandall CS, & Sherman JW (2016). On the scientific superiority of conceptual replications for scientific progress. Journal of Experimental Social Psychology, 66, 93– 99.
  2. Hedges LV, & Schauer JM (in press). Statistical analyses for studying replication: Meta-analytic perspectives. Psychological Methods, doi: 10.1037/met0000189. 30070547 · DOI
  3. Jones D (2010). A WEIRD view of human nature skews psychologists’ studies. Science, 328(5986), 1627. 20576866
  4. Lynch JG Jr, Bradlow ET, Huber JC, & Lehmann DR (2015). Reflections on the replication corner: In praise of conceptual replications. International Journal of Research in Marketing, 32(4), 333– 342.
  5. Mathur MB, Bart-Plange DJ, Aczel B, Bernstein MH, Ciunci A, Ebersole CR, (in press). Many Labs 5: Registered multisite replication of tempting-fate effects in Risen & Gilovich (2008). Advances in Methods and Practices in Psychological Science.
  6. Mathur MB & VanderWeele TJ (in press). New metrics for meta-analyses of heterogeneous effects. Statistics in Medicine. Preprint: 10.1002/sim.8057.. PMC6519385 30513552 · DOI
  7. Mathur MB & VanderWeele TJ (2017). New statistical metrics for multisite replication projects. Preprint: https://osf.io/w89s5/.
  8. Oppenheimer DM, & Monin B (2009). The retrospective gambler’s fallacy: Unlikely events, constructing the past, and multiple universes. Judgment and Decision Making, 4(5), 326.
  9. van Aert RC, & Van Assen MA (2017). Bayesian evaluation of effect size after replicating an original study. PLoS One, 12(4), e0175302. 28388646 PMC5384677
  10. Wasserstein RL, & Lazar NA (2016). The ASA’s statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129– 133.

Authors
Mathur, M. B., & VanderWeele, T. J.
Affiliations
Harvard University (US)
Corresponding author
Mathur, Maya B.
Venue
Psychological Methods
Published
2019-10
Volume
24
Issue
5
Pages
571-575
ISSN
  • 1082-989X (ISSN-L)
  • 1939-1463
OpenAlex
W2979112662
Cited by
13
References
9
Citations by year
2025: 1 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 6 · 2020: 1 · 2019: 1
FWCI
1.67
Type
journal article
Language
en
Open access
Locations
  • Psychological Methods (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institutes of Health (R01 CA222147)
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
replication success, replication study, heterogeneous effects, effect size comparison
MeSH terms
Research Design, Reproducibility of Results

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