Crowdsourcing hypothesis tests: making transparent how design choices shape research results

Justin F. Landy, Miaolei (Liam) Jia, Isabel L. Ding, Domenico Viganola, Warren Tierney, Anna Dreber, Magnus Johansson, Thomas Pfeiffer, Charles R. Ebersole, Quentin F. Gronau, Alexander Ly, Don van den Bergh, Maarten Marsman, Koen Derks, Eric-Jan Wagenmakers, Andrew Proctor, Daniel M. bartels, Felix Cheung, Andrei Cimpian, Simone DholeM. Brent Donnellan, Adam Hahn, Michael P. Hall, William Jiménez-Leal, David J. Johnson, Richard E Lucas, Benoît Monin, Andres Montealegre, Elizabeth Mullen, Jun Pang, Jennifer Ray, Diego A. Reinero, Jesse Reynolds, Walter Sowden, Daniel Storage, Runkun Su, Christina M. Tworek, Jay J. Van Bavel, Daniel Walco, Julian Wills, Xiaobing xu, Kai Chi Yam, Xiaoyu Yang, William A. Cunningham, Martin Schweinsberg, Molly Urwitz, The Crowdsourcing Hypothesis Test Collaboration

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from 2 separate large samples (total N > 15,000) were then randomly assigned to complete 1 version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: Materials from different teams rendered statistically significant effects in opposite directions for 4 of 5 hypotheses, with the narrowest range in estimates being d = −0.37 to + 0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for 2 hypotheses and a lack of support for 3 hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, whereas considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.
Original languageEnglish
Pages (from-to)451-479
Number of pages29
JournalPsychological Bulletin
Volume146
Issue number5
DOIs
Publication statusPublished - May 2020

Keywords

  • Conceptual replications
  • Crowdsourcing
  • Forecasting
  • Research robustness
  • Scientific transparency

Cite this

Landy, J. F., Jia, M. L., Ding, I. L., Viganola, D., Tierney, W., Dreber, A., Johansson, M., Pfeiffer, T., Ebersole, C. R., Gronau, Q. F., Ly, A., van den Bergh, D., Marsman, M., Derks, K., Wagenmakers, E-J., Proctor, A., bartels, D. M., Cheung, F., Cimpian, A., ... The Crowdsourcing Hypothesis Test Collaboration (2020). Crowdsourcing hypothesis tests: making transparent how design choices shape research results. Psychological Bulletin, 146(5), 451-479. https://doi.org/10.1037/bul0000220