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Crowdsourcing Hypothesis Tests: Making Transparent How Design Choices Shape Research Results

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https://hdl.handle.net/10037/21375
DOI
https://doi.org/10.1037/bul0000220
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Accepted manuscript version (PDF)
Date
2020-01-16
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Landy, Justin F; Jia, Miaolei; Ding, Isabel L; Viganola, Domenico; Tierney, Warren; Dreber, Anna; Johannesson, Magnus; Pfeiffer, Thomas; Ebersole, Charles R; Gronau, Quentin F; Pfuhl, Gerit; 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.; Jiménez-Leal, William; Johnson, David J.; Lucas, Richard E.; Monin, Benoît; 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.
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.
Description
©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. Please do not copy or cite without author's permission. The final article is available, upon publication, at: https://doi.apa.org/doi/10.1037/bul0000220
Publisher
American Psychological Association
Citation
Landy, Jia, Ding, Viganola, Tierney, Dreber, Johannesson, Pfeiffer, Ebersole, Gronau, Pfuhl. Crowdsourcing Hypothesis Tests: Making Transparent How Design Choices Shape Research Results. Psychological bulletin. 2020
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