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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*
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.

Information

Type
Research Article
Copyright
© The Experimental Research Section of the American Political Science Association 2019
Figure 0

Figure 1 Main Effects for Information Treatment.

NOTES: Higher average values are equivalent to less prejudiced responses. The PCA component ranges from −3.76 to 2.56. The thermometer ranges from 0 to 100. The patriotism outcome is a binary variable for just as or more patriotic vs. less patriotic. The policy outcomes are four-point scales with higher values indicating opposition. 95% c.i.
Figure 1

Figure 2 Robustness Checks for Main Effects.

NOTES: Plots show treatment effects with and without the terrorism prime (left) and non-PC license (right). The coefficients for “Difference” are the interactions from ordinary least squares (OLS) regression models that interact the treatment with the prime/license. Because the prime/license were randomly assigned, no control variables are included. PC refers to “Politically Correct.” 95% c.i.
Figure 2

Figure 3 Heterogeneous Effects.

Notes: Each plot is a separate OLS model. Models include control variables not shown. 95% c.i.
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