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Comparing meta-analyses and preregistered multiple-laboratory replication projects

An Author Correction to this article was published on 02 June 2020

This article has been updated


Many researchers rely on meta-analysis to summarize research evidence. However, there is a concern that publication bias and selective reporting may lead to biased meta-analytic effect sizes. We compare the results of meta-analyses to large-scale preregistered replications in psychology carried out at multiple laboratories. The multiple-laboratory replications provide precisely estimated effect sizes that do not suffer from publication bias or selective reporting. We searched the literature and identified 15 meta-analyses on the same topics as multiple-laboratory replications. We find that meta-analytic effect sizes are significantly different from replication effect sizes for 12 out of the 15 meta-replication pairs. These differences are systematic and, on average, meta-analytic effect sizes are almost three times as large as replication effect sizes. We also implement three methods of correcting meta-analysis for bias, but these methods do not substantively improve the meta-analytic results.

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Fig. 1: PRISMA flow diagram showing the number of meta-analyses considered for inclusion.
Fig. 2: Results of meta-analyses and replication studies.
Fig. 3: Mean effect size difference across the 15 meta-replication pairs in our sample, and robustness test and sub-group analyses of this difference.
Fig. 4: Comparison of effect sizes of the original studies replicated in the replication studies to meta-analytic effect sizes.
Fig. 5: Results for methods of correcting meta-analyses for bias.
Fig. 6: Estimated differences in meta-analytic and replication studies effect sizes for methods of correcting meta-analyses for bias.

Data availability

The data used in this paper are posted at the project’s OSF repository (link:

Code availability

The analysis code for all analyses are available at the project’s OSF repository (link:

Change history

  • 02 June 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


  1. 1.

    Siddaway, A. P., Wood, A. M. & Hedges, L. V. How to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annu. Rev. Psychol. 70, 747–770 (2019).

    PubMed  Google Scholar 

  2. 2.

    Cumming, G. The new statistics: why and how. Psychol. Sci. 25, 7–29 (2014).

    PubMed  Google Scholar 

  3. 3.

    Stanley, T. D. Wheat from chaff: meta-analysis as quantitative literature review. J. Econ. Perspect. 15, 131–150 (2001).

    Google Scholar 

  4. 4.

    Gurevitch, J., Koricheva, J., Nakagawa, S. & Stewart, G. Meta-analysis and the science of research synthesis. Nature 555, 175–182 (2018).

    CAS  PubMed  Google Scholar 

  5. 5.

    Camerer, C. F. et al. Evaluating replicability of laboratory experiments in economics. Science 351, 1433–1436 (2016).

    CAS  PubMed  Google Scholar 

  6. 6.

    Camerer, C. F. et al. Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nat. Hum. Behav. 2, 637 (2018).

    PubMed  Google Scholar 

  7. 7.

    Klein, R. A. et al. Investigating variation in replicability: a “Many Labs” replication project. Soc. Psychol. 45, 142–152 (2014).

    Google Scholar 

  8. 8.

    Klein, R. A. et al. Many Labs 2: investigating variation in replicability across samples and settings. Adv. Methods Pract. Psychol. Sci. 1, 443–490 (2018).

    Google Scholar 

  9. 9.

    Ebersole, C. R. et al. Many Labs 3: evaluating participant pool quality across the academic semester via replication. J. Exp. Soc. Psychol. 67, 68–82 (2016).

    Google Scholar 

  10. 10.

    Open Science Collaboration. Estimating the reproducibility of psychological science. Science 349, aac4716 (2015).

  11. 11.

    Duval, S. & Tweedie, R. A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. J. Am. Stat. Assoc. 95, 89–98 (2000).

    Google Scholar 

  12. 12.

    Ioannidis, J. P. Why most published research findings are false. PLoS Med. 2, e124 (2005).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Ioannidis, J. P. Why most discovered true associations are inflated. Epidemiology 19, 640–648 (2008).

    PubMed  Google Scholar 

  14. 14.

    Simmons, J. P., Nelson, L. D. & Simonsohn, U. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22, 1359–1366 (2011).

    PubMed  Google Scholar 

  15. 15.

    Gelman, A. & Carlin, J. Beyond power calculations: assessing type S (sign) and type M (magnitude) errors. Perspect. Psychol. Sci. 9, 641–651 (2014).

    PubMed  Google Scholar 

  16. 16.

    Gelman, A. & Loken, E. The statistical crisis in science. Am. Sci. 102, 460 (2014).

    Google Scholar 

  17. 17.

    Brodeur, A., Lé, M., Sangnier, M. & Zylberberg, Y. Star wars: the empirics strike back. Am. Econ. J. Appl. Econ. 8, 1–32 (2016).

    Google Scholar 

  18. 18.

    Andrews, I. & Kasy, M. Identification of and correction for publication bias. Am. Econ. Rev. 109, 2766–2794 (2019).

    Google Scholar 

  19. 19.

    Schäfer, T. & Schwarz, M. A. The meaningfulness of effect sizes in psychological research: differences between sub-disciplines and the impact of potential biases. Front. Psychol. 10, article 813 (2019).

    PubMed  Google Scholar 

  20. 20.

    John, L. K., Loewenstein, G. & Prelec, D. Measuring the prevalence of questionable research practices with incentives for truth telling. Psychol. Sci. 23, 524–532 (2012).

    PubMed  Google Scholar 

  21. 21.

    Franco, A., Malhotra, N. & Simonovits, G. Publication bias in the social sciences: unlocking the file drawer. Science 345, 1502–1505 (2014).

    CAS  PubMed  Google Scholar 

  22. 22.

    Franco, A., Malhotra, N. & Simonovits, G. Underreporting in political science survey experiments: comparing questionnaires to published results. Polit. Anal. 23, 306–312 (2015).

    Google Scholar 

  23. 23.

    Sterne, J. A., Gavaghan, D. & Egger, M. Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature. J. Clin. Epidemiol. 53, 1119–1129 (2000).

    CAS  PubMed  Google Scholar 

  24. 24.

    Rothstein, H. R., Sutton, A. J. & Borenstein, M. Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments (Wiley, 2005).

  25. 25.

    Schwarzer, G., Carpenter, J. R. & Rücker, G. in Meta-analysis with R. Use R! 107–141 (Springer, 2015).

  26. 26.

    Polanin, J. R., Tanner-Smith, E. E. & Hennessy, E. A. Estimating the difference between published and unpublished effect sizes: a meta-review. Rev. Educ. Res. 86, 207–236 (2016).

    Google Scholar 

  27. 27.

    Nelson, L. D., Simmons, J. & Simonsohn, U. Psychology’s renaissance. Annu. Rev. Psychol. 69, 511–534 (2018).

    PubMed  Google Scholar 

  28. 28.

    Vosgerau, J., Simonsohn, U., Nelson, L. D. & Simmons, J. P. 99% impossible: a valid, or falsifiable, internal meta-analysis. J. Exp. Psychol. Gen. 148, 1628 (2019).

    PubMed  Google Scholar 

  29. 29.

    Vevea, J. L. & Hedges, L. V. A general linear model for estimating effect size in the presence of publication bias. Psychometrika 60, 419–435 (1995).

    Google Scholar 

  30. 30.

    Hedges, L. V. Modeling publication selection effects in meta-analysis. Stat. Sci. 7, 246–255 (1992).

    Google Scholar 

  31. 31.

    Stanley, T. D. & Doucouliagos, H. Meta‐regression approximations to reduce publication selection bias. Res. Synth. Methods 5, 60–78 (2014).

    CAS  PubMed  Google Scholar 

  32. 32.

    Iyengar, S. & Greenhouse, J. B. Selection models and the file drawer problem. Stat. Sci. 3, 109–117 (1988).

    Google Scholar 

  33. 33.

    Simonsohn, U., Nelson, L. D. & Simmons, J. P. P-curve and effect size: correcting for publication bias using only significant results. Perspect. Psychol. Sci. 9, 666–681 (2014).

    PubMed  Google Scholar 

  34. 34.

    Carter, E. C., Schönbrodt, F. D., Gervais, W. M. & Hilgard, J. Correcting for bias in psychology: a comparison of meta-analytic methods. Adv. Methods Pract. Psychol. Sci. 2, 115–144 (2019).

    Google Scholar 

  35. 35.

    McShane, B. B., Böckenholt, U. & Hansen, K. T. Adjusting for publication bias in meta-analysis: an evaluation of selection methods and some cautionary notes. Perspect. Psychol. Sci. 11, 730–749 (2016).

    PubMed  Google Scholar 

  36. 36.

    Stanley, T. D. Limitations of PET-PEESE and other meta-analysis methods. Soc. Psychol. Personal. Sci. 8, 581–591 (2017).

    Google Scholar 

  37. 37.

    Simons, D. J., Holcombe, A. O. & Spellman, B. A. An introduction to registered replication reports at perspectives on psychological science. Perspect. Psychol. Sci. 9, 552–555 (2014).

    PubMed  Google Scholar 

  38. 38.

    Oppenheimer, D. M., Meyvis, T. & Davidenko, N. Instructional manipulation checks: detecting satisficing to increase statistical power. J. Exp. Soc. Psychol. 45, 867–872 (2009).

    Google Scholar 

  39. 39.

    Tversky, A. & Kahneman, D. The framing of decisions and the psychology of choice. Science 211, 453–458 (1981).

    CAS  PubMed  Google Scholar 

  40. 40.

    Husnu, S. & Crisp, R. J. Elaboration enhances the imagined contact effect. J. Exp. Soc. Psychol. 46, 943–950 (2010).

    Google Scholar 

  41. 41.

    Schwarz, N., Strack, F. & Mai, H.-P. Assimilation and contrast effects in part-whole question sequences: a conversational logic analysis. Public Opin. Q. 55, 3–23 (1991).

    Google Scholar 

  42. 42.

    Hauser, M., Cushman, F., Young, L., Kang‐Xing Jin, R. & Mikhail, J. A dissociation between moral judgments and justifications. Mind Lang. 22, 1–21 (2007).

    Google Scholar 

  43. 43.

    Critcher, C. R. & Gilovich, T. Incidental environmental anchors. J. Behav. Decis. Mak. 21, 241–251 (2008).

    Google Scholar 

  44. 44.

    Graham, J., Haidt, J. & Nosek, B. A. Liberals and conservatives rely on different sets of moral foundations. J. Personal. Soc. Psychol. 96, 1029 (2009).

    Google Scholar 

  45. 45.

    Jostmann, N. B., Lakens, D. & Schubert, T. W. Weight as an embodiment of importance. Psychol. Sci. 20, 1169–1174 (2009).

    PubMed  Google Scholar 

  46. 46.

    Monin, B. & Miller, D. T. Moral credentials and the expression of prejudice. J. Personal. Soc. Psychol. 81, 33 (2001).

    CAS  Google Scholar 

  47. 47.

    Schooler, J. W. & Engstler-Schooler, T. Y. Verbal overshadowing of visual memories: some things are better left unsaid. Cogn. Psychol. 22, 36–71 (1990).

    CAS  PubMed  Google Scholar 

  48. 48.

    Sripada, C., Kessler, D. & Jonides, J. Methylphenidate blocks effort-induced depletion of regulatory control in healthy volunteers. Psychol. Sci. 25, 1227–1234 (2014).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Rand, D. G., Greene, J. D. & Nowak, M. A. Spontaneous giving and calculated greed. Nature 489, 427 (2012).

    CAS  PubMed  Google Scholar 

  50. 50.

    Strack, F., Martin, L. L. & Stepper, S. Inhibiting and facilitating conditions of the human smile: a nonobtrusive test of the facial feedback hypothesis. J. Personal. Soc. Psychol. 54, 768 (1988).

    CAS  Google Scholar 

  51. 51.

    Srull, T. K. & Wyer, R. S. The role of category accessibility in the interpretation of information about persons: some determinants and implications. J. Personal. Soc. Psychol. 37, 1660 (1979).

    Google Scholar 

  52. 52.

    Mazar, N., Amir, O. & Ariely, D. The dishonesty of honest people: a theory of self-concept maintenance. J. Mark. Res. 45, 633–644 (2008).

    Google Scholar 

  53. 53.

    Hagger, M. S., Wood, C., Stiff, C. & Chatzisarantis, N. L. Ego depletion and the strength model of self-control: a meta-analysis. Psychol. Bull. 136, 495 (2010).

    PubMed  Google Scholar 

  54. 54.

    Feltz, A. & May, J. The means/side-effect distinction in moral cognition: a meta-analysis. Cognition 166, 314–327 (2017).

    PubMed  Google Scholar 

  55. 55.

    Meissner, C. A. & Brigham, J. C. A meta‐analysis of the verbal overshadowing effect in face identification. Appl. Cogn. Psychol. 15, 603–616 (2001).

    Google Scholar 

  56. 56.

    Kivikangas, J. M., Lönnqvist, J.-E. & Ravaja, N. Relationships between moral foundations and political orientation–local study and meta-analysis. in Annual Convention of Society for Personality and Social Psychology (2016).

  57. 57.

    DeCoster, J. & Claypool, H. M. A meta-analysis of priming effects on impression formation supporting a general model of informational biases. Personal. Soc. Psychol. Rev. 8, 2–27 (2004).

    Google Scholar 

  58. 58.

    Roth, S., Robbert, T. & Straus, L. On the sunk-cost effect in economic decision-making: a meta-analytic review. Bus. Res. 8, 99–138 (2015).

    Google Scholar 

  59. 59.

    Rabelo, A. L., Keller, V. N., Pilati, R. & Wicherts, J. M. No effect of weight on judgments of importance in the moral domain and evidence of publication bias from a meta-analysis. PloS One 10, e0134808 (2015).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Henriksson, K. A. C. Irrelevant Quantity Effects: A Meta-analysis. Master Thesis (California State University, Fresno, 2015).

  61. 61.

    Miles, E. & Crisp, R. J. A meta-analytic test of the imagined contact hypothesis. Group Process. Intergr. Relat. 17, 3–26 (2014).

    Google Scholar 

  62. 62.

    Belle, N. & Cantarelli, P. What causes unethical behavior? A meta-analysis to set an agenda for public administration research. Pub. Adm. Rev. 77, 327–339 (2017).

    Google Scholar 

  63. 63.

    Blanken, I., van de Ven, N. & Zeelenberg, M. A meta-analytic review of moral licensing. Pers. Soc. Psychol. Bull. 41, 540–558 (2015).

    PubMed  Google Scholar 

  64. 64.

    Rand, D. G. Cooperation, fast and slow: meta-analytic evidence for a theory of social heuristics and self-interested deliberation. Psychol. Sci. 27, 1192–1206 (2016).

    PubMed  Google Scholar 

  65. 65.

    Schimmack, U. & Oishi, S. The influence of chronically and temporarily accessible information on life satisfaction judgments. J. Pers. Soc. Psychol. 89, 395–406 (2005).

    PubMed  Google Scholar 

  66. 66.

    Coles, N. A., Larsen, J. T. & Lench, H. C. A meta-analysis of the facial feedback literature: effects of facial feedback on emotional experience are small and variable. Psychol. Bull. 145, 610–651 (2019).

    PubMed  Google Scholar 

  67. 67.

    Kühberger, A. The influence of framing on risky decisions: a meta-analysis. Org. Behav. Hum. Dec. Proc. 75, 23–55 (1998).

    Google Scholar 

  68. 68.

    Verschuere, B. et al. Registered replication report on Mazar, Amir, and Ariely (2008). Adv. Methods Pract. Psychol. Sci. 1, 299–317 (2018).

    Google Scholar 

  69. 69.

    Bouwmeester, S. et al. Registered replication report: Rand, Greene, and Nowak (2012). Perspect. Psychol. Sci. 12, 527–542 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    McCarthy, R. J. et al. Registered replication report on Srull and Wyer (1979). Adv. Methods Pract. Psychol. Sci. 1, 321–336 (2018).

    Google Scholar 

  71. 71.

    Wagenmakers, E.-J. et al. Registered replication report: Strack, Martin, & Stepper (1988). Perspect. Psychol. Sci. 11, 917–928 (2016).

    PubMed  Google Scholar 

  72. 72.

    Hagger, M. S. et al. A multilab preregistered replication of the ego-depletion effect. Perspect. Psychol. Sci. 11, 546–573 (2016).

    PubMed  Google Scholar 

  73. 73.

    Alogna, V. et al. Registered replication report: Schooler and Engstler-Schooler (1990). Perspect. Psychol. Sci. 9, 556–578 (2014).

    CAS  PubMed  Google Scholar 

  74. 74.

    Benjamin, D. J. et al. Redefine statistical significance. Nat. Hum. Behav. 2, 6 (2018).

    PubMed  Google Scholar 

  75. 75.

    Fanelli, D., Costas, R. & Ioannidis, J. P. Meta-assessment of bias in science. Proc. Natl Acad. Sci. USA 114, 3714–3719 (2017).

    CAS  PubMed  Google Scholar 

  76. 76.

    Augusteijn, H. E., van Aert, R. & van Assen, M. A. The effect of publication bias on the Q test and assessment of heterogeneity. Psychol. Methods 24, 116 (2019).

    PubMed  Google Scholar 

  77. 77.

    Stanley, T., Carter, E. C. & Doucouliagos, H. What meta-analyses reveal about the replicability of psychological research. Psychol. Bull. 144, 1325–1346 (2018).

    CAS  PubMed  Google Scholar 

  78. 78.

    van Aert, R. C., Wicherts, J. M. & van Assen, M. A. Conducting meta-analyses based on P values: reservations and recommendations for applying P-uniform and P-curve. Perspect. Psychol. Sci. 11, 713–729 (2016).

    PubMed  PubMed Central  Google Scholar 

  79. 79.

    Simonsohn, U., Nelson, L. D. & Simmons, J. P. P-curve: a key to the file-drawer. J. Exp. Psychol. Gen. 143, 534 (2014).

    PubMed  Google Scholar 

  80. 80.

    LeLorier, J., Gregoire, G., Benhaddad, A., Lapierre, J. & Derderian, F. Discrepancies between meta-analyses and subsequent large randomized, controlled trials. N. Engl. J. Med. 337, 536–542 (1997).

    CAS  PubMed  Google Scholar 

  81. 81.

    Nosek, B. A., Ebersole, C. R., DeHaven, A. C. & Mellor, D. T. The preregistration revolution. Proc. Natl Acad. Sci. USA 115, 2600–2606 (2018).

    CAS  PubMed  Google Scholar 

  82. 82.

    Mullen, B. Strength and immediacy of sources: a meta-analytic evaluation of the forgotten elements of social impact theory. J. Personal. Soc. Psychol. 48, 1458 (1985).

    Google Scholar 

  83. 83.

    Holleman, B. Wording effects in survey research using meta-analysis to explain the forbid/allow asymmetry. J. Quant. Linguist. 6, 29–40 (1999).

    Google Scholar 

  84. 84.

    Carter, E. C., Kofler, L. M., Forster, D. E. & McCullough, M. E. A series of meta-analytic tests of the depletion effect: self-control does not seem to rely on a limited resource. J. Exp. Psychol. Gen. 144, 796 (2015).

    PubMed  Google Scholar 

  85. 85.

    Baumeister, R. F., Bratslavsky, E. & Muraven, M. Ego depletion: is the active self a limited resource? J. Personal. Soc. Psychol. 74, 1252–1265 (2018).

    Google Scholar 

  86. 86.

    Thaler, R. Mental accounting and consumer choice. Mark. Sci. 4, 199–214 (1985).

    Google Scholar 

  87. 87.

    Borenstein, M., Hedges, L. V., Higgins, J. P. & Rothstein, H. R. Introduction to Meta-analysis (Wiley, 2011).

  88. 88.

    Galinsky, A. D., Magee, J. C., Inesi, M. E. & Gruenfeld, D. H. Power and perspectives not taken. Psychol. Sci. 17, 1068–1074 (2006).

    PubMed  Google Scholar 

  89. 89.

    Finkel, E. J., Rusbult, C. E., Kumashiro, M. & Hannon, P. A. Dealing with betrayal in close relationships: does commitment promote forgiveness? J. Personal. Soc. Psychol. 82, 956 (2002).

    Google Scholar 

  90. 90.

    Higgins, J. P., Thompson, S. G. & Spiegelhalter, D. J. A re‐evaluation of random‐effects meta‐analysis. J. R. Stat. Soc. Ser. A 172, 137–159 (2009).

    Google Scholar 

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For financial support we thank J. Wallander and the Tom Hedelius Foundation (grant no. P2015-0001:1), the Swedish Foundation for Humanities and Social Sciences (grant no. NHS14-1719:1) and the Meltzer Fund in Bergen. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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A.K., E.S. and M.J. designed research and wrote the paper. A.K. and E.S. collected and analysed data. All authors approved the final manuscript.

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Correspondence to Magnus Johannesson.

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Kvarven, A., Strømland, E. & Johannesson, M. Comparing meta-analyses and preregistered multiple-laboratory replication projects. Nat Hum Behav 4, 423–434 (2020).

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