Literature DB >> 31944796

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results.

Justin F Landy1, Miaolei Liam Jia2, Isabel L Ding3, Domenico Viganola4, Warren Tierney5, Anna Dreber6, Magnus Johannesson6, Thomas Pfeiffer7, Charles R Ebersole8, Quentin F Gronau9, Alexander Ly9, Don van den Bergh9, Maarten Marsman9, Koen Derks10, Eric-Jan Wagenmakers9, Andrew Proctor6, Daniel M Bartels11, Christopher W Bauman12, William J Brady13, Felix Cheung14, Andrei Cimpian13, Simone Dohle15, M Brent Donnellan16, Adam Hahn15, Michael P Hall17, William Jiménez-Leal18, David J Johnson19, Richard E Lucas16, Benoît Monin20, Andres Montealegre18, Elizabeth Mullen21, Jun Pang22, Jennifer Ray13, Diego A Reinero13, Jesse Reynolds23, Walter Sowden17, Daniel Storage24, Runkun Su25, Christina M Tworek26, Jay J Van Bavel13, Daniel Walco27, Julian Wills13, Xiaobing Xu28, Kai Chi Yam29, Xiaoyu Yang30, William A Cunningham31, Martin Schweinsberg32, Molly Urwitz6, Eric L Uhlmann33.   

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. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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Year:  2020        PMID: 31944796     DOI: 10.1037/bul0000220

Source DB:  PubMed          Journal:  Psychol Bull        ISSN: 0033-2909            Impact factor:   17.737


  17 in total

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2.  The spatial grounding of politics.

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3.  Putting your money where your self is: Connecting dimensions of closeness and theories of personal identity.

Authors:  Jan K Woike; Philip Collard; Bruce Hood
Journal:  PLoS One       Date:  2020-02-12       Impact factor: 3.240

Review 4.  Using mouse cursor tracking to investigate online cognition: Preserving methodological ingenuity while moving toward reproducible science.

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Journal:  Psychon Bull Rev       Date:  2020-12-14

5.  Bayesian model-averaged meta-analysis in medicine.

Authors:  František Bartoš; Quentin F Gronau; Bram Timmers; Willem M Otte; Alexander Ly; Eric-Jan Wagenmakers
Journal:  Stat Med       Date:  2021-10-27       Impact factor: 2.497

Review 6.  Two Challenges to "Embodied Cognition" Research And How to Overcome Them.

Authors:  Rolf A Zwaan
Journal:  J Cogn       Date:  2021-02-16

7.  Predicting replicability-Analysis of survey and prediction market data from large-scale forecasting projects.

Authors:  Michael Gordon; Domenico Viganola; Anna Dreber; Magnus Johannesson; Thomas Pfeiffer
Journal:  PLoS One       Date:  2021-04-14       Impact factor: 3.240

8.  Novel predictions arise from contradictions.

Authors:  Itai Yanai; Martin Lercher
Journal:  Genome Biol       Date:  2021-05-11       Impact factor: 13.583

9.  Using prediction markets to predict the outcomes in the Defense Advanced Research Projects Agency's next-generation social science programme.

Authors:  Domenico Viganola; Grant Buckles; Yiling Chen; Pablo Diego-Rosell; Magnus Johannesson; Brian A Nosek; Thomas Pfeiffer; Adam Siegel; Anna Dreber
Journal:  R Soc Open Sci       Date:  2021-07-14       Impact factor: 2.963

10.  Improving the reproducibility of findings by updating research methodology.

Authors:  Joseph Klein
Journal:  Qual Quant       Date:  2021-07-08
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