Did Trump's Proposal Align With How People Really Felt About Muslim Immigrants During the 2016 Election?

As candidates emerge for the 2024 US presidential election, Sentient tracks how partisan topics are framed and implicit associations on key American issues. We provide up-to-date information on current positioning, while drawing on past political studies to understand how priorities have evolved.

Warning: These Results Might Offend Your System 2 Sensibilities

Sentient Decision Science is a fascinating place to work. For those of you who don’t know us yet, essentially we quantify non-conscious processing in the mind and use the data to more accurately predict what people will do (e.g. what consumers will buy, which ads will go viral, who voters will vote for, etc.). This means we are regularly discovering novel insights and gaining new knowledge on the drivers of human behavior. Well, leading up to our study of the Republican debate in 2015, a Washington Post/ABC News poll released data on a survey that assessed American stated attitudes toward Donald Trump’s proposal to place a Ban on Muslim Immigration “until the government can figure out what’s going on.”

The data showed the following expressed attitudes of voters nationally to the proposal:
  • 60% “wrong thing to do” (46% strongly)
  • 36% ”support it” (25% strongly)
  • 4% no opinion

In the corridors of the Sentient offices, we looked at that data and immediately questioned whether an explicit response to a survey question could accurately capture true American sentiment on the issue. To answer that question, we designed a Sentient Prime implicit attitude module to include in our Republican debate study to assess implicit attitudes toward specific groups of people and topics (e.g. “muslim immigrants,” “assault weapons”) and proposed policies (“ban on Muslim immigration, “ban on assault weapons”).

Implicit Negative vs. Positive Emotional Associations

Implicit Associations Muslim Immigrants-1

The results were strikingly different than the explicit attitudes expressed in the Washington Post/ABC news poll: The Sentient Prime Implicit (SPI) score on the proposal to “ban on Muslim immigration” was positive at 103, representing 53% of those polled who held an implicit favorable attitude toward the proposal. For context, these results were compared to “ban on assault weapons” and “contain ISIS.” The data showed respondents also feel favorable on average toward those proposed actions (SPI: 115 and 108 respectively).

Implicit Associations Immigrants 2-1
To provide some additional context on these results, we also measured the implicit emotional associations with the subjects of the proposals. The implicit data revealed a significant non-conscious negative emotional association with “Muslim immigrants” equal to the negative emotions toward "assault weapons" and nearly as negative as the associations with “ISIS”. The SPI index of 86.7 toward “Muslim Immigrants” corresponds with 64% of Americans surveyed who hold a negative emotional association toward that phrase.

What may be even more interesting is the power of Sentient Prime to assess differences in implicit attitudes by segments of the population. When we split the data by Democrats versus Republicans the insights get even deeper. Interestingly, Democrats held a significantly different implicit attitude toward “Muslim immigrants” than Republicans, yet the average association was still negative (SPI: 89.8 Democrats vs. 78.8 Republicans). Further, both Democrats and Republicans held an equal and slightly positive association on average (SPI: 101 and 101 respectively) to the proposal for a “Ban on Muslim immigrants”).

Muslim Immigrants & ISIS-1


These results may be offensive to some, but the data indicates that Trump’s proposal which was also offensive to many, seemed to be tapping into non-conscious biases held by a significant proportion of the American population. Whether the proposal was appropriate or not is a separate question, but the implicit associations revealed here, are not something that should be ignored. Sentient Prime implicit research technology gives us access to drivers of behavior that simply cannot be captured in explicit survey questions. Applied within political polling, these methods offer a significant advantage to strategists who need to know the true emotional weight of issues facing a voting populace.

Can We Reveal the True Nature of Voter Fears?

It is fascinating to have watched, and participated in, the progression of implicit methods from their origins in studying stereotypes and biases in the ’90s, to their broad application to business questions over the past 10 years, and back into non-conscious bias assessment within political polling for the 2016 elections. The power of these methods to reveal the true nature of voter fears and attitudes holds both great promise and potential peril. If quantifying the non-conscious is used to better understand concerns and develop sound policy to address those concerns then it is easy to argue that this kind of knowledge is being used for “good.” However, I’m also certain that these results and the use of implicit techniques within political polling will also not be without controversy. Our position on the ethics of measuring the human non-conscious can be found here. In essence, we believe that the ethical issues are not around whether we are gaining access to human non-conscious processes (we all seek and obtain it on a daily basis in our automatic analyses of other people’s behavior), but rather we feel the ethical questions are around the ways in which you use the insight on non-conscious to influence the behaviors of others. To learn more about how to use Sentient Prime's implicit research technology in political polling or within your market research practice, or to see more of the results from this study, contact us

Disclaimer: The information provided is based on data tested in the past and may not reflect the current state of affairs. New developments, scientific discoveries, policy changes, or other factors may have emerged since then, which could significantly impact the accuracy and applicability of the information provided.

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