15 research outputs found

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

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    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 fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five 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 two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while 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.</div

    Study two: Results of the moderated regression analysis.

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    <p>Notes. <i>N</i> = 54; NFC  =  need for closure.</p><p>Situational need for closure was coded as 0 =  low, 1 =  high.</p><p>Coefficients are unstandardized B.</p><p>*p<.05;</p><p>**p<.01.</p><p>Study two: Results of the moderated regression analysis.</p

    Study one: Means, standard deviations and correlations among variables.

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    <p>Notes. <i>N</i> = 60.</p><p>*p<.05;</p><p>**p<.01.</p><p>Study one: Means, standard deviations and correlations among variables.</p

    Study two: Means, standard deviations and correlations among variables.

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    <p>Notes. <i>N</i> = 54</p><p>*p<.05;</p><p>**p<.01.</p><p>Study two: Means, standard deviations and correlations among variables.</p

    Means of positive global evaluation of the political candidate as a function of message framing and regulatory focus.

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    <p>Means of positive global evaluation of the political candidate as a function of message framing and regulatory focus.</p

    Means of prevision of a positive impact of the election of the candidate on the immigration as a function of message framing and regulatory focus.

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    <p>Means of prevision of a positive impact of the election of the candidate on the immigration as a function of message framing and regulatory focus.</p

    Means of implicit attitudes toward nuclear energy as a function of message framing and regulatory focus.

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    <p>Means of implicit attitudes toward nuclear energy as a function of message framing and regulatory focus.</p

    Study one: Results of the hierarchical regression analysis.

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    <p>Notes. <i>N</i> = 60. Coefficients are standardized beta.</p><p>*p<.05;</p><p>**p<.01.</p><p>Study one: Results of the hierarchical regression analysis.</p
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