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A Change of Heart? Why Individual-Level Public Opinion Shifted Against Trump’s “Muslim Ban”

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Abstract

Public opinion research suggests that rapid and significant individual-level fluctuations in opinions toward various policies is fairly unexpected absent methodological artifacts. While this may generally be the case, some political actions can and do face tremendous backlash, potentially impacting public evaluations. Leveraging broadcast and newspaper transcripts as well as a unique two-wave panel study we demonstrate that a non-random, rapid shift in opinions occurred shortly after President Donald Trump signed executive order 13769 into law, which barred individuals from seven predominantly Muslim countries from entering the United States for 90 days. The ban set off a fury of protests across U.S. cities and airports, garnering tremendous media attention and discussion. Drawing insights from literature on priming, we claim that an influx of new information portraying the “Muslim Ban” at odds with inclusive elements of American identity prompted some citizens to shift their attitudes. Our study highlights the potential broad political effects of mass movements and protests as it pertains to policies that impact racialized minority groups, and suggests that preferences can shift quickly in response to changing political circumstances.

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Notes

  1. https://www.washingtonpost.com/blogs/plum-line/wp/2017/02/07/a-new-poll-shows-a-surprisingly-big-public-swing-against-trumps-muslim-ban/?utm_term=.ba8a426ac235.

  2. https://www.nytimes.com/2017/01/28/opinion/donald-trumps-muslim-ban-is-cowardly-and-dangerous.html.

  3. https://www.washingtonpost.com/news/the-fix/wp/2017/01/30/republicans-insist-this-isnt-a-muslim-ban-trump-and-giuliani-arent-helping-them-make-that-case/?utm_term=.0d6792c46f49.

  4. http://www.cnn.com/2017/01/29/politics/trump-travel-ban-congress-reaction/index.html.

  5. Our search of newspaper articles include all articles that contain the word “Muslim.”

  6. For example, freedom of religion was explicitly in the founding charters of the following colonies: Maryland (1634), Rhode Island (1636), Connecticut (1636), Flushing, Queens (1645), New Jersey (1682), and Pennsylvania (1682).

  7. Given some Don’t Know/Refused responses to our ban question in T1 and T2, our main dependent variables in T2 has n = 280 responses. No statistically significant demographic differences emerged across the two waves as a result of response rates (see Table 6 in the Appendix).

  8. Because of this we also analyze our data to weighted CCES proportions; our substantive findings hold.

  9. https://www.washingtonpost.com/blogs/plum-line/wp/2017/02/07/a-new-poll-shows-a-surprisingly-big-public-swing-against-trumps-muslim-ban/?utm_term=.ba8a426ac235.

  10. Research indicates that Google Analytics is an accurate method to assess what populations are thinking about: https://campaignstops.blogs.nytimes.com/2012/06/09/how-racist-are-we-ask-google/.

  11. Given the ordinal nature of our immigration ban wording, we do estimate the “Muslim Ban” baseline models (1 and 2) as ordered logit, which we present in Table 9 in the Appendix. Our core findings remain unchanged.

  12. We also subset wave 1 to interviews from the earliest date, January 24, before the announcements of these executive orders. In both cases the difference of means t-test comparisons across waves are not statistically significant.

  13. For sample size purposes, we dummy education and income. We present models in the Appendix where we treat these variables in their more continuous format. See Tables 14 and 15. Our findings remain unchanged. The overall sample size for the change models drop, too, on account of some missing data throughout the dataset. We also conducted a hot deck imputation on the missing data and re-estimated the analysis; our substantive findings did not change.

  14. Also see Tables 14, 15, 16, 17 in the Appendix for additional analyses.

  15. This finding speaks to affective polarization and the interactive relationship between partisanship and the non-ideological construct of American identity. Although we note that we interacted the two, due to sample size limitations, we did not find a statistically significant effect. Future research with larger sample sizes should further examine this relationship.

  16. https://d25d2506sfb94s.cloudfront.net/cumulus_uploads/document/aipb1h7oe9/tabs_Religious_Discrimination_20160325.pdf.

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Author information

Authors and Affiliations

  1. University of California, Riverside, CA, USA

    Loren Collingwood

  2. Michigan State University, East Lansing, MI, USA

    Nazita Lajevardi

  3. University of Delaware, Newark, DE, USA

    Kassra A. R. Oskooii

Authors
  1. Loren Collingwood
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  2. Nazita Lajevardi
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  3. Kassra A. R. Oskooii
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Corresponding author

Correspondence to Nazita Lajevardi.

Additional information

The data and replication code are publicly available at https://www.collingwoodresearch.com/data.html, under the Replication Data heading. Authors are listed in alphabetical order; authorship is equal. The authors are grateful for all the insightful feedback provided by the anonymous reviewers. A special thanks is also extended to Jennifer Merolla, Ali Valenzuela, Ben Bishin, Dave Redlawsk, Dan Biggers, Gina Gustavsson, Aubrey Westfall, Ben Bagozzi, John Kuk, Nick Weller and all of the participants at UCSB PRIEC, UCLA mini-conference on the Study of Race and Ethnicity, UCR Mass Behavior workshop, and APSA panel on Muslims in the American Imagination.

Appendix

Appendix

Control variables:

  • DV: President Trump’s executive order restricting immigration from Syria, Iran, Iraq, Libya, Yemen, Somalia, and Sudan. Strongly disagree (1); Somewhat disagree (2); Neither agree nor disagree (3); Somewhat agree (4); Strongly agree (5).

  • President Trump’s executive order allowing for the Keystone and Dakota Access Pipelines. (1); Somewhat disagree (2); Neither agree nor disagree (3); Somewhat agree (4); Strongly agree (5).

  • President Trump’s executive order to build a wall on the southern border. (1); Somewhat disagree (2); Neither agree nor disagree (3); Somewhat agree (4); Strongly agree (5).

  • Income: What is your family’s annual income? Under $20,000 a year (1); Between $20,000 and $40,000 a year (2); Between $40,000 and $60,000 a year (3) Between $60,000 and $80,000 a year (4) Between $80,000 and $120,000 a year (5); Over $120,000 a year (6). $60K or less = 1; else = 0.

  • Education: What is the highest level of education you have completed? No High School Degree (1); High School Degree (2); Some College; (3) 2-Year College Degree (4) 4-Year College Degree (5); Post Graduate Degree (6). Some College or less = 1; else = 0.

  • Which political party do you most align with? (1 = Democrat; else = 0; 1 = Republican; else = 0; Independent/other = base category)

  • American Identity (additive scale): To what extent do you agree or disagree with the following statements—strongly disagree (1), somewhat disagree (2), neither agree nor disagree (3), somewhat agree (4), or strongly agree (5)? The scale runs from 4 (no American identity) to 20 (high American identity):

    • My American identity is an important part of myself.

    • Being an American is an important part of how I see myself.

    • I see myself as a typical American person.

    • I am proud to be an American.

  • Muslim Affect Scale: With respect to Muslim Americans, how much do you agree or disagree with the following statements—strongly disagree, somewhat disagree, neither agree nor disagree, somewhat agree, strongly agree? (statements (re)coded so that high values indicate positive affect)

    • Muslim Americans integrate successfully into American culture.

    • Muslim Americans sometimes do not have the best interests of Americans at heart.

    • Muslims living in the US should be subject to more surveillance than others.

    • Muslim Americans, in general, tend to be more violent than other people.

    • Most Muslim Americans reject jihad and violence.

    • Most Muslim Americans lack basic English language skills.

    • Most Muslim Americans are not terrorists.

    • Wearing headscarves should be banned in all public places.

    • Muslim Americans do a good job of speaking out against Islamic terrorism.

  • Age: In what year were you born (2016-answer)

  • Female: What is your gender? Male (0) or Female (1)

  • White: What racial group best describes you? White (1) else = 0.

  • Voted Trump?: Did you vote in the 2016 presidential election? Yes, I voted for Hillary Clinton (0); Yes, I voted for Donald Trump (1); Yes, I voted for a third party (0); No, I did not vote. (0)

  • Do you approve of the way President Trump’s is handling his job as President? 1 = Approve; Else = 0.

See Appendix Tables 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 and 18

Table 5 Did the recent demonstrations and public statements against the recent immigration and visa ban impact your views toward immigration policy?
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Table 6 Wave 1 and wave 2 difference of means comparisons
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Table 7 Unweighted MTurk and CCES data (voters)
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Table 8 OLS predictors of Mexico Wall and Keystone Pipeline T1–T2 change attitudes (disagree–agree): (1) President Trump’s executive order to build a wall on the southern border. (2) President Trump’s executive order allowing for the Keystone and Dakota Access Pipelines
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Table 9 Ordered logistic predictors of Muslim Ban attitudes (disagree-agree): President Trump’s executive order restricting immigration from Syria, Iran, Iraq, Libya, Yemen, Somalia, and Sudan
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Table 10 Demographic difference of mean comparisons across respondents who saw demonstrations on TV versus those who did not
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Table 11 OLS predictors of Muslim Ban attitudes (disagree-agree): President Trump’s executive order restricting immigration from Syria, Iran, Iraq, Libya, Yemen, Somalia, and Sudan
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Table 12 Predictors of American identity shift as function of ban attitudes—DV: scale of American identity items
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Table 13 OLS predictors of Muslim Ban attitudes (disagree-agree): President Trump’s executive order restricting immigration from Syria, Iran, Iraq, Libya, Yemen, Somalia, and Sudan
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Table 14 OLS predictors of Muslim Ban attitudes (disagree-agree): President Trump’s executive order restricting immigration from Syria, Iran, Iraq, Libya, Yemen, Somalia, and Sudan
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Table 15 OLS predictors of American identity shift as function of ban attitudes–DV: scale of American identity items
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Table 16 GLM predictors of Muslim Ban attitudes (disagree-agree): President Trump’s executive order restricting immigration from Syria, Iran, Iraq, Libya, Yemen, Somalia, and Sudan
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Table 17 GLM predictors of American identity shift as function of ban attitudes—DV: scale of American identity items
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Table 18 Mean party differences on ban change among high identifiers
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Collingwood, L., Lajevardi, N. & Oskooii, K.A.R. A Change of Heart? Why Individual-Level Public Opinion Shifted Against Trump’s “Muslim Ban”. Polit Behav 40, 1035–1072 (2018). https://doi.org/10.1007/s11109-017-9439-z

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