Anti-Immigrant Sentiment and BREXIT

In this blog post, we are going to discuss about the process of engineering metrics to capture the two major concepts in our research question using the British Election Study panel data:

How do attitudes toward immigrants & immigration influence the volatility of opinions on BREXIT?

Anti-Immigration Sentiment (AIS)

To quantify individual’s immigration sentiment, we identified 4 survey questions that explicitly asked about respondents’ attitudes towards immigrants and immigration. Given that the 4 survey questions were asked across multiple waves, to compute one immigration index on a person level, we first need to confirm that one’s immigration attitude does not vary drastically over time. In particular, we want to look at the range of attitude change. If respondants’ immigration sentiment vary within a small range over time, we can assume that by taking the average of an individual’s responses across multiple waves we have an accurate representation of their attitude at any point of time.

In practice, we computed the range of change by taking an individual’s maximum and minimum responses over the period in which they participated in the survey. We defined ‘tolerable change’ as an individual has changed in their reponses at most by 1 level for a 5-level response questions (e.g. from “Disagree” to “Strongly Disagree”), or at most 2 levels for a 7-level response questions (e.g. from 2 to 4 on a scale of 1 to 7) in the entire course of their survey. Any changes more than described above are defined as ‘significant change’.

Fig.1 below shows that more than 75% of the respondants exhibit no change or tolerable change in their immigration attitude. This is true for all 4 survey questions we identified. Therefore, we decide to quantify an individual’s immigration attitude using their mean response.

The four questions are:

  • immigEcon: “Do you think immigration is good or bad for Britain’s economy?”
  • immigCultural: “Do you think that immigration undermines or enriches Britain’s cultural life?”
  • immigrantsWelfareState: “How much do you agree or disagree with the following statements? ‘Immigrants are a burden on the welfare state?’”
  • changeImmig: “Do you think that immigration are getting higher, getting lower or staying about the same?”

Fig.2 Correlation matrix between responses to the 4 survery questions on immigration sentiments.

Fig.2 shows us that the responses to the first 3 questions are highly correlated (correlation coefficient > 0.75). This helps us make the decision to compute an overall index to quantify Anti-immigrant Sentiment (AIS) using responses from the first 3 questions.

Volatility of BREXIT Voting Preference

Next, we want to find a way to quantify opinion volatility in respondants’ attitude towards Brexit. Except for wave 5, the survey asks respondents “If there was a referendum on Britain’s membership of the European Union$turnoutText, how do you think you would vote?”. We flag the responses if it is of a polarized switch from “stay” to “leave” or from “leave” to “stay” as compared to their previous responses in last wave.

We computed two person-level metrics: switch_ratio and if_switch. switch_ratio is calculated by using the number of switches an individual had divided by the number of waves they participated – 1. This is because for n waves an indiviudal participated in the survey, there are only n – 1 waves they can possibly switch their voting preference. It measures how frequent an individual switches their opinion. if_switch is a binary variable that indicates if an individual has switche their voting preference at all.

In practice, we found that if_switch is much more helpful in differentiating respondants in terms of opinion volatility. Firstly, approximately only 1 in every 7 respondants switched their voting preference from one side to the other side. (1 indicates switch and 0 otherwise).

##     0     1 
## 36892  7932

Therefore, in effect, the distribution of switch_ratio looks like:

Such a highly skewed distribution makes it difficult for switch_ratio to effectively represent opinion volatility in modeling tools.

In addition, we looked at the relationship between switch_ratio and immigration sentiment (AIS).

As the graphs above suggest, AIS remains relatively constant regardless of switch_ratio. We observe volatility of AIS when switch_ratio exceeds 0.5, but the extremely small sample size in that range undermines the significance of such observation. Given that we do not observe a difference in immigration sentiment in respondents no matter how frequent they switch their voting preference, we hypothesize that the frequency of switch is not as critical as whether individuals switch their opinion at all. Therfore, we deicde to use if_switch instead of switch_ratio to advance our investigation.

Later on, we use combine if_swtich and respondents’ final vote to categorize respondants into 4 different voter types:

  • Voters who always voted for “stay”
  • Voters who always voted for “leave”
  • Voters who have switched their opinion and voted for “stay”
  • Voters who have switched their opinion and voted for “leave”

With this categorization, we developed an interactive Shiny application to directly visualize the difference in immigration sentiments amongst 4 different voter types.

Source: British Election Panel Study Data

New DASIL Space in the Humanities and Social Studies Center (HSSC)

Data — from a single digit to terabytes of information — increasingly shapes decisions on public policy to the way we individually go about our daily lives. To some this is exciting; to others, intimidating.

The Data Analysis and Social Inquiry Lab (DASIL) helps students and faculty members integrate data analysis into both classroom work and research by facilitating workshops, helping students collect and analyze data, and offering software training and data-set preparation.

“Since the fall of 2017, DASIL has been active in providing services to over 170 clients, including students and faculty,” says Xavier Escandell, associate professor of anthropology and faculty director for DASIL. “We have also successfully continued our support of the institution at large.” (Click here to continue reading).

Data Across the Curriculum: The Explanatory Power of Data in Global Development & Geography

The field of geography is split into two camps: critical scholars, who are skeptical of data because they believe it silences certain voices within society and fails to explain process and context, and empirical scholars, who incorporate data to create empirical models that explain geographic concepts and trends.  Leif  Brottem, Assistant Professor of Political Science with a PhD in Geography, is a firm believer in the importance of both critical and empirical approaches.  Data analysis can compensate for and expand upon the limits of text and qualitative evidence. His focus on data analysis as a tool that illustrates narrative is evident in the work of each of his three classes, Introduction to Global Development Studies (GDS), Introduction to Geographical Analysis and Cartography, and Climate Change, Development, and Environment.

In his Introduction to Global Development Studies, for instance, Brottem utilizes infographics & charts to explain basic concepts, and utilizes data tools such as GapMinder to illustrate change over time and regional differences pertaining to a variety of development indicators. His students also complete two data analysis exercises as a part of the class: one exercise asks students to study the relationship between economic development and social development indicators, and the second has students explore different aspects of population dynamics such as carrying capacity, limits to growth and the determinants of population growth.

In Brottem’s Introduction to Geographical Analysis and Cartography course, students learn both the basic critical perspectives on how to evaluate maps and understand their overt and covert messages and practical techniques for making maps using Geographical Information Systems software.  Students complete in-class exercises and take-home labs that require creating data and using data to solve problems.


Finally, in Climate Change, Development, and Environment, Brottem utilizes data analysis in the form of topic-modeling: students investigate textual trends in various sources, from tweets to scholarly articles, using the MALLET topic model package. In addition, his students also work with Nvivo to conduct further qualitative analysis, and GIS to visualize spatial trends.

Working with data builds data literacy, a marketable and necessary skill in the real world that Brottem says isn’t typically developed in a liberal arts settings. Building data literacy is especially important in his introductory classes, because he has students who wouldn’t otherwise be exposed to data, and aims to get them comfortable with using data and reduce their fears of data, numbers, and data analysis. Brottem strongly believes that data is a powerful explanatory tool that helps students think of different ways to look at the world and their studies, beyond theory.

10 Suggestions for Making an Effective Poster


Written papers are the traditional way to share research results at professional meetings, but poster sessions have been gaining popularity in many fields. Posters are particularly effective for sharing quantitative data, as they provide a good format for presenting data visualizations and allow readers to peruse the information at leisure.  For students they are a great teaching tool, as preparing a good poster also requires clear and concise writing.

Making a poster is easy, but making a really good poster is hard.  I have found the guidelines below helpful to students.  The most important piece of advice, however, is the one true for all writing—write, read and revise; write, read and revise; write, read and revise!

  1. Make your poster using PowerPoint. This will allow you to put in text via text boxes as well as to paste in charts, graphs, tables, maps, and pictures.  It is easy! To get your pictures and text boxes to line up consistently, use snap to grid.  In the Format tab choose Arrange>>Align and then Grid Setting. Select to view the grid and to snap to the grid.  You can set the grid size here as well.
  1. Use a single slide. In the Design Tab pick Page Setup, select custom, and then set the width and height to maximize your slide, given the locally-available paper size. At Grinnell the paper width available is 36”, so we set the width to 45” and the height to 36”.  Use “landscape” for your orientation.
  1. As in a written paper, have a descriptive title. Put the title (in 68 point type or larger) at the top of the poster.  Place your name and college affiliation in slightly smaller type immediately below it.
  1. The exact sections of the poster will vary some depending on the project, but include an abstract placed either under the title or in the upper left column.
  1. As in a written paper, be sure you have a good thesis and present it early in the poster, support it with evidence, then remind your audience of it as you conclude. Finish with a minimum of citations and acknowledgements in the lower right hand corner.
  1. Posters should read sequentially from the upper left, down the left column, then down the central column (if you have one) and finally down the right column. Alternative layouts are possible, but the order in which the poster is read must be obvious.
  1. Use a large font–a minimum of 28 point.
  1. Limit the number of words. Be concise and think of much of your text as captions for illustrations.
  1. Use lots of charts, graphs, maps, and other pictures. Be sure to label your figures and refer to them in the text.
  1. Make your poster attractive. Use color.  Pay attention to layout.  Do not have large empty areas.


The #Holidays, According to Twitter

The holiday season is among us, and people are flocking to social media to share their thoughts about the holidays with friends, family, and followers. Using Nvivo, a text-analysis software, DASIL tracked tweets with the hashtags #Kwanzaa, #Hanukkah, and #Christmas published on Dec. 10th and earlier to create word clouds that demonstrate the top 100 words most frequently associated with each holiday.









For Christmas, tweets highlight how highly commercialized the nature of the holiday has become. The most common words found alongside “Christmas” include “win,” “giveaway,” “luck,” “enter,” “chance,” and others, attesting to how advertisements for giveaways and contests to win prizes are dominating talk of Christmas in the Twittersphere. A handful of tweets reference the holiday’s secular traditions, with words like “decked,” “festive,” “tree,” and “lighting.”

Like Christmas tweets, Hanukkah tweets mention words relating to its traditions (“menorah,” “candles,” “lighting,” and “latkes”). Hanukkah tweets differ from Christmas tweets by centering instead on sentimental values and inclusivity, with words like “happy,” “everyone,” “celebrating,” and “family.” Some Hanukkah tweeters are also using the holiday to highlight political issues, with mentions of “Netanyahu,” Israel’s prime minister, as well as “police,” “western,” “border,” and “#muslim.” Surprisingly, both Hanukkah and Christmas tweets completely ignore their religious roots, with no mentions of words relating to the respective origins of each holiday.

The words associated with Kwanzaa, the week-long celebration of African heritage by the West African diaspora, are a grab-bag: words like “celebration,” “promoted,” “happy,” “family,” and “submissions” reveal a mixture of sentimental and commercial connotations. Interestingly, the “#blacklivesmatter” hashtag crops up as one common word associated with Kwanzaa, showing how Kwanzaa, like Hanukkah, is being used as a vehicle to spotlight social and political issues revolving around racial injustice.

All in all, the big takeaways from this short text analysis are that, of the three current holidays, Christmas is the most commercial.  Hannukkah celebrates family and inclusivity, but also has a political edge, while Kwanzaa has a less focused identity.