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      Submission or Rebellion? Disentangling the Relationships of Anxiety, Attitudes Toward Authorities, and Right-Wing Populist Party Support

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          Abstract

          The success of right-wing populist parties (RPPs) is often attributed to their deployment of the rhetoric of fear that capitalizes on societal crisis and corresponding anxieties. However, empirical evidence on the relationship between anxiety and support for RPP (RPP support) remains inconclusive. We argue that right-wing authoritarian (RWA) and populist attitudes imply contradicting views on authority. Anti-elitism, a subdimension of populist attitudes, implies rebellion against established authorities; however, RWA submission relates to the inclination to obey authorities. These contradictory attitudes may account for the mixed results. In relation to anxiety, both rebellion and submission are conceived as defensive responses, but their relation to RPP support is different because the reactions to authority they induce are antithetical. Moreover, we differentiate between two forms of anxiety as sources of RPP support, which are often conflated in empirical studies: situational anxiety arising in response to specific threats and diffuse anxiety or a general sense of anxiety. We draw on mass survey data, including a survey experiment, that examines how anxiety drives support for the German right-wing populist party Alternative für Deutschland (Alternative for Germany, AfD) via attitudes toward authority. The path analyses support our hypotheses and reveal that anti-elitism mediates the positive relationship between anxiety and voter support for the AfD. At the same time, authoritarian submission increases with anxiety, but—unlike anti-elitism—it is negatively associated with AfD support. Furthermore, the two paths are confirmed for situational and diffuse anxiety, with the relative importance of authoritarian submission and anti-elitism varying according to the form of anxiety.

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          Most cited references78

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          The Populist Zeitgeist

          Cas Mudde (2004)
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            Populist Radical Right Parties in Europe

            Cas Mudde (2007)
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              Scaling regression inputs by dividing by two standard deviations.

              Interpretation of regression coefficients is sensitive to the scale of the inputs. One method often used to place input variables on a common scale is to divide each numeric variable by its standard deviation. Here we propose dividing each numeric variable by two times its standard deviation, so that the generic comparison is with inputs equal to the mean +/-1 standard deviation. The resulting coefficients are then directly comparable for untransformed binary predictors. We have implemented the procedure as a function in R. We illustrate the method with two simple analyses that are typical of applied modeling: a linear regression of data from the National Election Study and a multilevel logistic regression of data on the prevalence of rodents in New York City apartments. We recommend our rescaling as a default option--an improvement upon the usual approach of including variables in whatever way they are coded in the data file--so that the magnitudes of coefficients can be directly compared as a matter of routine statistical practice. (c) 2007 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                American Behavioral Scientist
                American Behavioral Scientist
                SAGE Publications
                0002-7642
                1552-3381
                April 18 2024
                Affiliations
                [1 ]German Center for Integration and Migration Research (DeZIM), Berlin, Germany
                [2 ]WZB Berlin Social Science Center, Berlin, Germany
                [3 ]Freie Universität Berlin, Cluster of Excellence “Contestations of the Liberal Script (SCRIPTS),” Berlin, Germany
                [4 ]University of Vienna, Wien, Austria
                Article
                10.1177/00027642241240717
                2069ea9a-d541-40e2-8202-dad46662aab6
                © 2024

                https://creativecommons.org/licenses/by-nc/4.0/

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