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Overestimating Reported Prejudice Causes Democrats to Believe Disadvantaged Groups Are Less Electable.

  • Published In: Political Psychology, 2023, v. 44, n. 1. P. 95 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Mercier, Brett; Celniker, Jared B.; Shariff, Azim F. 3 of 3

Abstract

Four studies show that Democrats overestimate the explicit prejudice reported by the American electorate, leading them to perceive presidential candidates from disadvantaged groups as less electable. Study 1 (MTurk; n = 728) found that Democrats overestimated the percentage of Americans who say they would not vote for presidential candidates from disadvantaged groups. Study 2 (MTurk; n = 597) replicated this finding and demonstrated that Democrats who perceive high levels of explicit prejudice toward a group also believe presidential candidates from that group are less electable. Moreover, Democrats who more frequently interacted with Republicans were more accurate in estimating the amount of explicit prejudice reported by Republicans, Democrats, and Americans in general. Studies 3A (Prolific; n = 930) and 3B (YouGov; n = 747) found that presenting information about true levels of reported prejudice made Democrats believe generic presidential candidates from disadvantaged groups would be more electable. We did not find evidence that information about true levels of reported prejudice affected Democrats' beliefs about the electability of specific candidates in the 2020 Democratic Primary or their support for these candidates. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Political Psychology. 2023/02, Vol. 44, Issue 1, p95
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:0162-895X
  • DOI:10.1111/pops.12820
  • Accession Number:161524939
  • Copyright Statement:Copyright of Political Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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