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Countering Misperceptions to Reduce Prejudice: An Experiment on Attitudes toward Muslim Americans

Published online by Cambridge University Press:  02 September 2019

Scott Williamson Open the ORCID record for Scott Williamson [Opens in a new window]
Scott Williamson*
Affiliation:
Department of Political Science, Stanford University, USA, e-mail: scottw3@stanford.edu, Twitter: @scottrw630
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Abstract

Muslim Americans constitute one of the United States’ most vulnerable minority groups, facing frequent discrimination from both the public and the government. Despite this vulnerability, few studies evaluate interventions for reducing prejudice against Muslim Americans. Building from an insightful literature on the sources of prejudice against Muslim Americans, this paper tests whether attitudes can be improved with information countering misperceptions of the community as particularly foreign, threatening, and disloyal to the United States. The experimental treatment modestly improved attitudes, including among some subgroups predisposed to prejudice against Muslim Americans. However, the treatment struggled to change policy views, and it demonstrated some vulnerability to social desirability bias and priming on terrorism threats. The findings suggest that information campaigns addressing misperceptions can help to reduce prejudice on the margins, but primarily in less politicized contexts.

Keywords

prejudice reductionMuslim AmericansexperimentsAmerican politics
Type
Research Article
Information
Journal of Experimental Political Science , Volume 7 , Issue 3 , Winter 2020 , pp. 167 - 178
Copyright
© The Experimental Research Section of the American Political Science Association 2019

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Footnotes

I thank the Lab for the Study of American Values at Stanford University for funding and supporting this project. I also thank Mike Tomz, Paul Sniderman, Nazita Lajevardi, Abdulkader Sinno, Ala’ Alrababa’h, Jonathan Chu, Nathan Lee, Salma Mousa, Christiana Parrreira, Seth Werfel, the Stanford Lab for the Study of American Values, and the Stanford Immigration Policy Lab for helpful comments. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-114747. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. The data, code, and any additional materials required to replicate all analyses in this article (Williamson 2019) are available at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at: https://doi.org/10.7910/DVN/GKW5Q5. The study was approved under Stanford IRB Protocol 40682. The author declares they have no conflicts of interest.

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