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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. Strasak, Alexander M. & Zaman, Qamruz & Marinell, Gerhard & Pfeiffer, Karl P. & Ulmer, Hanno, 2007. "[MEDICINE] The Use of Statistics in Medical Research: A Comparison of The New England Journal of Medicine and Nature Medicine," The American Statistician, American Statistical Association, vol. 61, pages 47-55, February.
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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. 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.
    3. Genevieve Pham-Kanter & Darren E Zinner & Eric G Campbell, 2014. "Codifying Collegiality: Recent Developments in Data Sharing Policy in the Life Sciences," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-8, September.
    4. 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.
    5. Franca Agnoli & Jelte M Wicherts & Coosje L S Veldkamp & Paolo Albiero & Roberto Cubelli, 2017. "Questionable research practices among italian research psychologists," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-17, March.
    6. 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.
    7. Andrew F Magee & Michael R May & Brian R Moore, 2014. "The Dawn of Open Access to Phylogenetic Data," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-10, October.
    8. 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.
    9. Simon Robin Evans, 2016. "Gauging the Purported Costs of Public Data Archiving for Long-Term Population Studies," PLOS Biology, Public Library of Science, vol. 14(4), pages 1-9, April.
    10. Victoria Stodden & Peixuan Guo & Zhaokun Ma, 2013. "Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.

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