Crowdsourcing hypothesis tests: Making transparent how design choices shape research results
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Post-printDate
2020-01-16Author
Landy, Justin F.Jia, Miaolei
Ding, Isabel L.
Viganola, Domenico
Tierney, Warren
Dreber, Anna
Johannesson, Magnus
Pfeiffer, Thomas
Ebersole, Charles R.
Gronau, Quentin F.
Ly, Alexander
van den Bergh, Don
Marsman, Maarten
Derks, Koen
Wagenmakers, Eric-Jan
Proctor, Andrew
Bartels, Daniel M.
Bauman, Christopher W.
Brady, William J.
Cheung, Felix
Cimpian, Andrei
Dohle, Simone
Donnellan, M. Brent
Hahn, Adam
Hall, Michael P.
Jimenez-Leal, William
Johnson, David J.
Lucas, Richard E.
Monin, Benoit
Montealegre, Andres
Mullen, Elizabeth
Pang, Jun
Ray, Jennifer
Reinero, Diego A.
Reynolds, Jesse
Sowden, Walter
Storage, Daniel
Su, Runkun
Tworek, Christina M.
Van Bavel, Jay J.
Walco, Daniel
Wills, Julian
Xu, Xiaobing
Yam, Kai Chi
Yang, Xiaoyu
Cunningham, William A.
Schweinsberg, Martin
Urwitz, Molly
Uhlmann, Eric L.
The Crowdsourcing Hypothesis Tests Collaboration
Baker, Bradley

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Sport and Recreation ManagementPermanent link to this record
http://hdl.handle.net/20.500.12613/7111; http://dx.doi.org/10.34944/dspace/7091
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https://doi.org/10.1037/bul0000220Abstract
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.Citation
Crowdsourcing Hypothesis Tests Collaboration, & Albers, C. (2020). Crowdsourcing hypothesis tests: Making transparent how design choices shape research results. Psychological Bulletin, 146(5), 451–479. https://doi.org/10.1037/bul0000220Citation to related work
© American Psychological Association, [2020]. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. The final article is available, upon publication, at: https://doi.org/10.1037/bul0000220Has part
Psychological Bulletin, Vol. 146, Iss. 5ADA compliance
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http://dx.doi.org/10.34944/dspace/7091