Posts tagged Will it Unstructure?
What Americans Really Think about Trump’s Immigration Ban and Why

Text Analysis of What People Say in Their Own Words Reveals More Than Multiple-Choice Surveys It’s been just over a week since President Trump issued his controversial immigration order, and the ban continues to dominate the news and social media.

But while the fate of Executive Order 13769—“Protecting the Nation from Foreign Terrorist Entry into the United States”—is being hashed out in federal court, another fierce battle is being waged in the court of public opinion.

In a stampede to assess where the American people stand on this issue, the news networks have rolled out a parade of polls. And so, too, once again, the accuracy of polling data has been called into question by pundits on both sides of the issue.

Notably, on Monday morning the president, himself, tweeted the following:

Any negative polls are fake news, just like the CNN, ABC, NBC polls in the election. Sorry, people want border security and extreme vetting.

— Donald J. Trump (@realDonaldTrump) February 6, 2017

Majority Flips Depending on the Poll

It’s easy to question the accuracy of polls when they don’t agree.

Although on the whole these polls all indicate that support is pretty evenly divided on the issue, the all-important sound bite of where the majority of Americans stand on the Trump immigration moratorium flips depending on the source:

  • NBC ran with an Ipsos/Reuters poll that found the majority of Americans (49% vs. 41%) support the ban.

  • Fox News went with similar results from a poll by Quinnipiac College (48% in favor vs. 42% opposed).

  • CNN publicized results from an ORC Poll with the majority opposed to the ban (53% vs. 47%).

  • A widely reported Gallup poll found the majority of Americans oppose the order (55% to 42%).

There are a number of possible reasons for these differences, of course. It could be the way the question was framed (as suggested in this Washington Post column); it could be the timing (much has transpired and has been said between the dates these polls were taken); maybe the culprit is sample; perhaps modality played a part (some were done online, others by phone with an interviewer), etc.

My guess is that all of these factors to varying degrees account for the differences, but the one thing all of these polls share is that the instrument was quantitative.

So, I decided to see what if anything happens when we try to “unstructure” this question, which seemingly lends itself so perfectly to a multiple-choice format. How would an open-ended version of the same question compare with the results from the structured version? Would it add anything of value?

Part I: A Multiple-Choice Benchmark

The first thing we did was to run a quantitative poll as a comparator using a U.S. online nationally representative sample* of n=1,531 (a larger sample, by the way, than any of the aforementioned polls used).

In carefully considering how the question was framed in the other polls and how it’s being discussed in the media, we decided on the following wording:

“Q. How do you personally feel about Trump's latest Executive Order 13769 ‘Protecting the Nation from Foreign Terrorist Entry into the United States’ aka ‘A Muslim Ban’”?

We also went with the simplest and most straightforward closed-ended Likert scale—a standard five-point agreement scale. Below are the results:

TextAnalyticsTrumpOrder1.png

Given a five-point scale, the most popular answer by respondents (36%) was “strongly disagree.” Interestingly, the least popular choice was “somewhat disagree” (6.6%).

Collapsing “strongly” and “somewhat” (see chart below) we found 4% more Americans (43%) disagree with Trump’s Executive Order than agree with it (39%). A sizeable number (18%) indicated they aren’t sure/don’t know.

Trump-Text-Analytics-2.png

Will It Unstructure? - A Text Analytics PollTM

Next, we asked another 1500 respondents from the same U.S. nationally online representative source* EXACTLY the same question, but instead of providing choices for them to select from, we asked them to reply in an open-ended comment box in their own words.

We ran the resulting comments through OdinText, with the following initial results:

Trump-OdinText.png

As you can see, the results from the unstructured responses were remarkably close to those from structured question. In fact, the open-ended responses suggest Americans are slightly closer to equally divided on the issue, though slightly more disagree (a statistically significant percentage given the sample size).

This, however, is where the similarities between unstructured and structured data end.

While there is nothing more to be done with the Likert scale data, the unstructured question data analysis has just begun…

Low-Incidence Insights are Hardly Incidental

It’s worth noting here that OdinText was able to identify and quantify many important, but low-incidence insights—positive and negative— that would have been treated as outliers in a limited code-base and dismissed by human coders:

  • “Embarrassment/Shame” (0.2%)

  • “Just Temporary” (0.5%)

  • “Un-American” (0.9%)

  • “Just Certain/Specific Countries” (0.9%)

  • “Unconstitutional/Illegal” (2%)

  • “Not a Muslim Ban/Stop Calling it that” (2.9%)

An Emotionally-Charged Policy

EMOTIONAL-SENTIMENT-ANALYSIS-TRUMP.png

It shouldn’t come as a surprise to anyone that emotions around this particular policy run exceptionally high.

OdinText quickly quantified the emotions expressed in people’s comments, and you can see that while there certainly is a lot of anger—negative comments are spread across anger, fear/anxiety and sadness—there is also a significant amount of joy.

What the heck does “joy” entail, you ask? It means that enough people expressed unbridled enthusiasm for the policy along the lines of, “I love it!” or “It’s about time!” or “Finally, a president who makes good on his campaign promises!”

Understanding the Why Behind People’s Positions

Last, but certainly not least, asking the same question in an open-ended format where respondents can reply in their own words enables us to also understand why people feel the way they do.

We can then quantify those sentiments using text analytics and see the results in context in a way that would not have been possible using a multiple-choice format.

Here are a few examples from those who disagree with the order:

  • “Just plain wrong. It scored points with his base, but it made all Americans look heartless and xenophobic in the eyes of the world.”

  • “Absolutely and unequivocally unconstitutional. The foundation, literally the reason the first European settlers came to this land, was to escape religious persecution.”

  • “I don't like and it was poorly thought out. I understand the need for vetting, but this was an absolute mess.”

  • “I think it is an overly confident action that will do more harm than good.”

  • “I understand that Trump's intentions mean well, but his order is just discriminating. I fear that war is among us, and although I try my best to stay neutral, it's difficult to support his actions.”

Here are a few from those who agree:

  • “I feel it could have been handled better but I agree. Let’s make sure they are here documented correctly and backgrounds thoroughly checked.”

  • “I feel sometimes things need to be done to demonstrate seriousness. I do feel bad for the law abiding that it affects.”

  • “Initially I thought it was ridiculous, but after researching the facts associated with it, I'm fine with it. Trump campaigned on increasing security, so it shouldn't be a surprise. I think it is reasonable to take a period of time to standardize and enforce the vetting process.”

  • “I feel that it is not a bad idea. The only part that concerns me is taking away from living the American Dream for those that aren’t terrorists.”

  • “good but needed more explanation”

  • “OK with it - waiting to see how it pans out over the next few weeks”

  • “I think it is good, as long as it is temporary so that we can better vet those who would come to the U.S.”

And just as importantly, yet oft-overlooked those who aren’t completely sure:

  • “not my circus”

  • “While the thought is good and just for our safety, the implementation was flawed, much like communism.”

Final Thoughts: What Have we Learned?

First of all, we saw that the results in the open-ended format replicated those of the structured question. With a total sample of 3000, these results are statistically significant.

Second, we found that while emotions run high for people on both sides of this issue, comments from those who disagree with the ban tended to be more emotionally charged than from those who agreed with the ban. I would add here that some of the former group tended not to distinguish between their feelings about President Trump and the policy.

We also discovered that supporters of the ban appear to be better informed about the specifics of the order than those who oppose it. In fact, a significant number of the former group in their responses took the time to explain why referring to the order as “a Muslim ban” is inaccurate and how this misconception clouds the issue.

Lastly, we found that both supporters and detractors are concerned about the order’s implementation.

Let me know what you think. I’d be happy to dig into this data a bit more. In addition, if anyone is curious and would like to do a follow-up analysis, please contact me to discuss the raw data file.

@TomHCAnderson

Ps. Stay tuned for Part II of this study, where we’ll explore what the rest of the world thinks about the order!

*Note: Responses (n=3,000) were collected via Google Surveys. Google Surveys allow researchers to reach a validated (U.S. General Population Representative) sample by intercepting people attempting to access high-quality online content—such as news, entertainment and reference sites—or who have downloaded the Google Opinion Rewards mobile app. These users answer up to 10 questions in exchange for access to the content or Google Play credit. Google provides additional respondent information across a variety of variables including source/publisher category, gender, age, geography, urban density, income, parental status, response time as well as google calculated weighting. Results are +/- 1.79% accurate at the 95% confidence interval.

Top 2017 New Year’s Resolutions Text Analyzed (In Their Own Words)

Will it Unstructure? Part I of a New Series of Text Analytics Tests Happy New Year!

As I was preparing to celebrate the New Year with my family and pondering the year ahead, my mind wandered to all of those Top New Year’s Resolutions lists that you see the last week in December every year. It seems to me that the same resolutions with very similar incidence populate those lists each year, usually with something around diet and/or exercise as the most popular resolution.

After spending several minutes investigating, it occurred to me that these lists are almost always compiled using quantitative instruments with static choice answers pre-defined by researchers—therefore limited in options and often biased.

Here’s a good example of a study that has been repeated now for a few years by online financial institution GOBankingRates.com.

While their 2017 survey was focused solely on financial resolutions, their 2016 survey was broader and determined that “Live Life to The Fullest” was the most popular resolution (45.7%), followed by “Live a Healthier life” (41.1%) etc. [see chart below].

NewYearsRessolutionsStructured-300x188.png

The question I had, of course, was what would this look like if you didn’t force people to pick from a handful of arbitrary, pre-defined choices?

Will It Unstructure?

You may be familiar with the outlandish but wildly popular “Will it Blend?” video series by Blendtec, where founder Tom Dickson attempts to blend everything from iPhones to marbles. It’s a wacky, yet compelling way to demonstrate how sturdy these blenders are!

Well, today I’m announcing a new series of experiments that we’re calling “Will it Unstructure?

The idea here is to take structured questions from surveys, polls and so forth we come across and ask: Will it Unstructure? In other words, will asking the same question in an open-ended fashion yield the same or different results?

(In the future, we’ll cover more of these. Please send us suggestions for structured questions you’d like us to test!)

Will New Year’s Resolutions Unstructure? A Text Analytics PollTM

So, back to those Top New Year’s Resolution lists. Let’s find out: Will it Unstructure?

Over New Year’s weekend we surveyed n=1,536 respondents*, asking them the same question that was asked in the GoBankingRates.com example I referenced earlier: “What are your 2017 resolutions?”

*Representative online general population sample sourced via Google Surveys.

Below is a table of the text comments quickly analyzed by OdinText.

WillItUnstructure1OdinText.png

As you can see, there’s a lot more to be learned when you allow people to respond unaided and in their own words. In fact, we see a very different picture of what really matters to people in the coming year.

Note: The GoBankingRates.com survey allowed people to select more than one answer.

Predictably, Health (Diet and/or Exercise) came in first, but with a staggeringly lower incidence of mentions compared to the percent of respondents who selected it on the GoBankingRates.com survey: 19.4% vs. 80.7%.

Moreover, we found that ALL of the top resolution categories in the GoBankingRates.com example actually appeared DRAMATICALLY less frequently when respondents were given the opportunity to answer the same question unaided and in their own words:

  • “Living life to the fullest” = 1.1% vs. 45.7%

  • Financial Improvement (make/save more and/or cut debt) = 2.9% vs. 57.6%

  • Spend more time with family/friends = 0.2% vs. 33.2%

Furthermore, the second most-mentioned resolution in our study didn’t even appear in the GoBankingRates.com example!

What we’ll call “Spirituality” here—a mix of sentiments around being kinder to others, staying positive, and finding inner peace—appeared in 8.3% of responses, eclipsing each of the top resolutions from the GoBankingRates.com example except diet/exercise.

After that we see a wide variety of equally often mentioned and sometimes contradictory resolutions. Now, bear in mind that some of these responses—“Drink more alcohol,” for example—were probably made tongue-in-cheek. Interestingly, even in those cases, more than one person said the same thing, which suggests it may mean something more. (I.e., could this have been filed under “Have Fun/Live Life to the Fullest”?)

These replies are all low incidence, sure, but they certainly provide a fuller picture. For instance, who would’ve predicted that “getting a driver’s license/permit” or “getting married” would be a New Year’s resolution? I would add that among these low incidence mentions, a text analysis a way to understand the relative differences in frequency between various answers.

Disturbingly, 0.3% (five people) said their 2017 resolution is to die. Whether or not these responses were in jest or serious is debatable. Our figure is coincidentally not so far off from estimates from reputable sources with expertise on the subject. For example, according to Emory University, in the past year approx. 1.0% of the U.S. population (2.3 million people) developed a suicide plan and 0.5% (1 million people) attempted suicide.

All of this said, obviously the GoBankingRate.com survey was not a scientific instrument. We selected it at random from a lot of similar “Top New Year’s Resolutions” surveys available.

These results are all, of course, relatively subject to interpretation and we can debate them on a number of fronts, but at the end of the day it’s unmistakably clear that a quantitative instrument with a finite set of choices tells an entirely different story than people do when they have the opportunity to respond unaided and in their own words.

Bonus: Top Three Most Important Events of 2016

Since the whole New Year’s resolutions topic is a little overdone, I ran an additional question just for fun: “Name the Three Most Important Things That Happened in 2016.”

Here are the results from OdinText ranked in order of occurrence in 2016..

MostMemorableEventsOf2016textanalysis.png

If I had to answer this question myself I would probably say Donald Trump winning the U.S. Presidential Election, Russian aggression/hacking and Brexit.

But, again, not everyone places the same weight on events. So here’s yet another example of how much more we can learn when we ask people to reply unaided, in their own words.

Thanks for reading!

REMINDER: Let me know what questions you would like us to use for future posts on the “Will it Unstructure?” series!

Wishing you and yours a happy, healthy new year!

@TomHCAnderson

Tom H. C. Anderson