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      It’s their fault: Partisan attribution bias and its association with voting intentions

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          Abstract

          This research examined how people explain major outcomes of political consequence (e.g., economic growth, rising inequality). We argue that people attribute positive outcomes more and negative outcomes less to their own political party than to an opposing party. We conducted two studies, one before the 2016 U.S. presidential election ( N = 244) and another before the 2020 election ( N = 249 registered voters), that examined attributions across a wide array of outcomes. As predicted, a robust partisan attribution bias emerged in both studies. Although the bias was largely equivalent among Democrats and Republicans, it was magnified among those with more extreme political ideology. Further, the bias predicted unique variance in voting intentions and significantly mediated the link between political ideology and voting. In sum, these data suggest that partisan allegiances systemically bias attributions in a group-favoring direction. We discuss implications of these findings for emerging research on political social cognition.

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          Most cited references6

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          Nebraska symposium on motivation.

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            Social Cognition: from brains to culture

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              Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach

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                Author and article information

                Contributors
                Journal
                Group Processes & Intergroup Relations
                Group Processes & Intergroup Relations
                SAGE Publications
                1368-4302
                1461-7188
                April 01 2021
                : 136843022199008
                Affiliations
                [1 ]University of North Carolina at Greensboro, USA
                [2 ]Pennsylvania State University Abington, USA
                Article
                10.1177/1368430221990084
                71748e97-92bb-4e63-af3d-2ba267ef62f6
                © 2021

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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