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      Conservatives and Liberals have Similar Physiological Responses to Threats

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          Abstract

          About a decade ago a study documented that conservatives have stronger physiological responses to threatening stimuli than liberals. This work launched a paradigm aimed at uncovering the biological roots of ideology. Despite wide-ranging scientific and popular impact, independent laboratories have not replicated the study. We conducted a preregistered direct replication (N=202) and conceptual replications in the United States (N=352) and the Netherlands (N=81). Our analyses do not support the conclusions of the original study, nor do we find evidence for broader claims regarding the effect of disgust and the existence of a physiological trait. Rather than studying unconscious responses as the “real” predispositions, alignment between conscious and unconscious responses promise deeper insights in the emotional roots of ideology.

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          Most cited references35

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          Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015

          Being able to replicate scientific findings is crucial for scientific progress1-15. We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science between 2010 and 201516-36. The replications follow analysis plans reviewed by the original authors and pre-registered prior to the replications. The replications are high powered, with sample sizes on average about five times higher than in the original studies. We find a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size. Replicability varies between 12 (57%) and 14 (67%) studies for complementary replicability indicators. Consistent with these results, the estimated true-positive rate is 67% in a Bayesian analysis. The relative effect size of true positives is estimated to be 71%, suggesting that both false positives and inflated effect sizes of true positives contribute to imperfect reproducibility. Furthermore, we find that peer beliefs of replicability are strongly related to replicability, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.
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            Investigating Variation in Replicability

            Although replication is a central tenet of science, direct replications are rare in psychology. This research tested variation in the replicability of 13 classic and contemporary effects across 36 independent samples totaling 6,344 participants. In the aggregate, 10 effects replicated consistently. One effect – imagined contact reducing prejudice – showed weak support for replicability. And two effects – flag priming influencing conservatism and currency priming influencing system justification – did not replicate. We compared whether the conditions such as lab versus online or US versus international sample predicted effect magnitudes. By and large they did not. The results of this small sample of effects suggest that replicability is more dependent on the effect itself than on the sample and setting used to investigate the effect.
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              Bayesian hypothesis testing for psychologists: a tutorial on the Savage-Dickey method.

              In the field of cognitive psychology, the p-value hypothesis test has established a stranglehold on statistical reporting. This is unfortunate, as the p-value provides at best a rough estimate of the evidence that the data provide for the presence of an experimental effect. An alternative and arguably more appropriate measure of evidence is conveyed by a Bayesian hypothesis test, which prefers the model with the highest average likelihood. One of the main problems with this Bayesian hypothesis test, however, is that it often requires relatively sophisticated numerical methods for its computation. Here we draw attention to the Savage-Dickey density ratio method, a method that can be used to compute the result of a Bayesian hypothesis test for nested models and under certain plausible restrictions on the parameter priors. Practical examples demonstrate the method's validity, generality, and flexibility. Copyright 2009 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                101697750
                Nat Hum Behav
                Nat Hum Behav
                Nature human behaviour
                2397-3374
                08 January 2020
                10 February 2020
                June 2020
                10 August 2020
                : 4
                : 6
                : 613-621
                Affiliations
                [1 ]Amsterdam School of Communication Research, University of Amsterdam, Nieuwe Achtergracht 166, 1001 NG, Amsterdam, the Netherlands
                [2 ]Department of Political Science, University of Amsterdam, Nieuwe Achtergracht 166, 1001 NG, Amsterdam, the Netherlands
                [3 ]Center for American Women and Politics, Rutgers University, 191 Ryders Lane, New Brunswick, New Jersey, US
                [4 ]Department of Political Science, Temple University, 1115 Polett Walk, PA 19122, Philadelphia, USA
                Author notes
                [* ]Corresponding author: Bert N. Bakker ( b.n.bakker@ 123456uva.nl )
                Article
                EMS85387
                10.1038/s41562-020-0823-z
                7306406
                32042109
                21e0d96f-1cd8-4a61-9482-68407c0d31eb

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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