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Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results

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  • Jelte M Wicherts
  • Marjan Bakker
  • Dylan Molenaar

Abstract

Background: The widespread reluctance to share published research data is often hypothesized to be due to the authors' fear that reanalysis may expose errors in their work or may produce conclusions that contradict their own. However, these hypotheses have not previously been studied systematically. Methods and Findings: We related the reluctance to share research data for reanalysis to 1148 statistically significant results reported in 49 papers published in two major psychology journals. We found the reluctance to share data to be associated with weaker evidence (against the null hypothesis of no effect) and a higher prevalence of apparent errors in the reporting of statistical results. The unwillingness to share data was particularly clear when reporting errors had a bearing on statistical significance. Conclusions: Our findings on the basis of psychological papers suggest that statistical results are particularly hard to verify when reanalysis is more likely to lead to contrasting conclusions. This highlights the importance of establishing mandatory data archiving policies.

Suggested Citation

  • Jelte M Wicherts & Marjan Bakker & Dylan Molenaar, 2011. "Willingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-7, November.
    • Handle: RePEc:plo:pone00:0026828
    DOI: 10.1371/journal.pone.0026828
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    References listed on IDEAS

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    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
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    Cited by:

    1. Coosje L S Veldkamp & Michèle B Nuijten & Linda Dominguez-Alvarez & Marcel A L M van Assen & Jelte M Wicherts, 2014. "Statistical Reporting Errors and Collaboration on Statistical Analyses in Psychological Science," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-19, December.
    2. Peter Pütz & Stephan B. Bruns, 2021. "The (Non‐)Significance Of Reporting Errors In Economics: Evidence From Three Top Journals," Journal of Economic Surveys, Wiley Blackwell, vol. 35(1), pages 348-373, February.
    3. Benjamin D K Wood & Rui Müller & Annette N Brown, 2018. "Push button replication: Is impact evaluation evidence for international development verifiable?," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-15, December.
    4. Aguinis, Herman & Banks, George C. & Rogelberg, Steven G. & Cascio, Wayne F., 2020. "Actionable recommendations for narrowing the science-practice gap in open science," Organizational Behavior and Human Decision Processes, Elsevier, vol. 158(C), pages 27-35.
    5. Keiko Kurata & Mamiko Matsubayashi & Shinji Mine, 2017. "Identifying the Complex Position of Research Data and Data Sharing Among Researchers in Natural Science," SAGE Open, , vol. 7(3), pages 21582440177, July.
    6. Antonia Krefeld-Schwalb & Benjamin Scheibehenne, 2023. "Tighter nets for smaller fishes? Mapping the development of statistical practices in consumer research between 2008 and 2020," Marketing Letters, Springer, vol. 34(3), pages 351-365, September.
    7. Hensel, Przemysław G., 2021. "Reproducibility and replicability crisis: How management compares to psychology and economics – A systematic review of literature," European Management Journal, Elsevier, vol. 39(5), pages 577-594.
    8. Giorgi, Francesca, 2021. "A new method to explore inferential risks associated with each study in a meta-analysis: An approach based on Design Analysis," Thesis Commons n5y8b_v1, Center for Open Science.
    9. Ryan P Womack, 2015. "Research Data in Core Journals in Biology, Chemistry, Mathematics, and Physics," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.
    10. Wicherts, Jelte M. & Veldkamp, Coosje Lisabet Sterre & Augusteijn, Hilde & Bakker, Marjan & van Aert, Robbie Cornelis Maria & van Assen, Marcel A. L. M., 2016. "Degrees of freedom in planning, running, analyzing, and reporting psychological studies A checklist to avoid p-hacking," OSF Preprints umq8d, Center for Open Science.
    11. Michal Krawczyk & Ernesto Reuben, 2012. "(Un)Available upon Request: Field Experiment on Researchers' Willingness to Share Supplementary Materials," Natural Field Experiments 00689, The Field Experiments Website.
    12. Pfenninger, Stefan & DeCarolis, Joseph & Hirth, Lion & Quoilin, Sylvain & Staffell, Iain, 2017. "The importance of open data and software: Is energy research lagging behind?," Energy Policy, Elsevier, vol. 101(C), pages 211-215.
    13. Bennett Kleinberg & Bruno Verschuere, 2015. "Memory Detection 2.0: The First Web-Based Memory Detection Test," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-17, April.
    14. Yulin Yu & Daniel M. Romero, 2024. "Does the Use of Unusual Combinations of Datasets Contribute to Greater Scientific Impact?," Papers 2402.05024, arXiv.org, revised Sep 2024.
    15. Matteo Colombo & Georgi Duev & Michèle B Nuijten & Jan Sprenger, 2018. "Statistical reporting inconsistencies in experimental philosophy," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-12, April.
    16. Kraft-Todd, Gordon T. & Rand, David G., 2021. "Practice what you preach: Credibility-enhancing displays and the growth of open science," Organizational Behavior and Human Decision Processes, Elsevier, vol. 164(C), pages 1-10.
    17. Dominique G Roche & Loeske E B Kruuk & Robert Lanfear & Sandra A Binning, 2015. "Public Data Archiving in Ecology and Evolution: How Well Are We Doing?," PLOS Biology, Public Library of Science, vol. 13(11), pages 1-12, November.
    18. Esther Maassen & Marcel A L M van Assen & Michèle B Nuijten & Anton Olsson-Collentine & Jelte M Wicherts, 2020. "Reproducibility of individual effect sizes in meta-analyses in psychology," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-18, May.
    19. Klaas Sijtsma, 2016. "Playing with Data—Or How to Discourage Questionable Research Practices and Stimulate Researchers to Do Things Right," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 1-15, March.
    20. Colombo, Matteo & Duev, Georgi & Nuijten, M.B. & Sprenger, Jan, 2018. "Statistical reporting inconsistencies in experimental philosophy," Other publications TiSEM 075f5696-ae1a-4aae-9e17-c, Tilburg University, School of Economics and Management.
    21. Irwin Waldman & Scott Lilienfeld, 2016. "Thinking About Data, Research Methods, and Statistical Analyses: Commentary on Sijtsma’s (2014) “Playing with Data”," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 16-26, March.
    22. Giorgi, Francesca, 2021. "A new method to explore inferential risks associated with each study in a meta-analysis: An approach based on Design Analysis," Thesis Commons n5y8b, Center for Open Science.
    23. Wicherts, Jelte M. & Veldkamp, Coosje Lisabet Sterre & Augusteijn, Hilde Elisabeth Maria & Bakker, Marjan & van Aert, Robbie Cornelis Maria & van Assen, Marcel A. L. M., 2016. "Degrees of freedom in planning, running, analyzing, and reporting psychological studies A checklist to avoid p-hacking," OSF Preprints umq8d_v1, Center for Open Science.

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