Political Research (POL252)

Midterm Assessment 2020
Political Research (POL252)
Instructions
• The coursework will be posted on QMPlus on 19 February 2019, and is due on 8 March at 23:55. Please
follow all designated SPIR submission guidelines for online submission as detailed on the POL252
QMPlus page. Standard late submission penalties apply.
• This is an assessed piece of coursework (worth 40% of your final module mark) for the POL252 module;
collaboration and/or discussion of the coursework with anyone is strictly prohibited. The rules for
plagiarism apply and any cases of suspected plagiarism of published work or the work of classmates will
be taken seriously.
• As this is an assessed piece of work, you may not email/ask the course tutors questions about the
coursework.
• Along with the coursework itself, the datasets for the coursework can be found in the POL252 page on
QMPlus.
• Coursework should be submitted via the appropriate link on the course QMPlus page.
– Please remember to state ONLY your student number on your coursework. Your name must not
appear on your coursework.
• Answers should be written in complete sentences. Be sure to answer all parts of the questions posed
and interpret the results.
• The word count for this assessment is 2000 words. This does not include the appendix (i.e. the R
code), or any words or numbers contained in plots and tables. Please note that full sentences included
in tables will form part of the word count. You WILL NOT be penalised for writing less than 2,000
words. But you WILL be penalised if you write more than 2,000 words.
• Please submit your work in a single document. Create an appendix section at the end which contains
all the R code needed to reproduce your results (you do not need to include the code that failed to run,
but just the cleaned-up version. Your code has to work when we run it). Failure to include the R code
means that the coursework will be marked incomplete.
• You may assume the methods you have used (e.g. descriptive statistics, t-tests, etc) are understood by
the reader and do not need definitions, but you do need to explain the intuition of these methods.
• Round all numbers to two digits after the decimal point.
• Do not copy and paste any brute R output (e.g. qt(.975, 21)) into your answers. Create a minimally
formatted table, if appropriate.
• Assign every table and figure a title and a number and refer to the number in the text when discussing
a specific figure or table.
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Structure of the blog post
Your response should read like a single piece (i.e. as a post and not as separate answers). The main parts
should be:
1. A short introduction explaining the task and the main findings.
2. A brief justification for theories posited below. You should include some references to back them up.
3. The results, where you include your table and graphs (see below). This is the section where you interpret
your results.
4. A conclusion section where you discuss your findings and propose other potential explanations for the
Brexit Vote (again, see below).
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Dataset
BrexitResults.RData
The following list of variables is your codebook:
Variable Description

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Project
Motivation
You are a prominent academic that works on voting behaviour in the UK. This is July 2016 and you have
been contacted by a major news outlet to write a blog post explaining your main theories about the reasons
behind the Brexit vote. After some careful considerations, you have three main theories that you would like
to test and write up the results:
1. This is an anti-London reaction, which is reflected by a large difference in the votes for the referendum
in London, compared to the rest of the country.
2. This is the result of rising immigration. People living in areas with a higher proportion of immigrants
voted for Brexit as a way to regain control on immigration.
3. This is the work of UKIP. Their surprising results in 2015 were simply a warning of what was about to
come. UKIP vote is a good predictor of the referendum outcome.
Specific tasks
In order to answer these questions, you should first examine the relevant variables individually (i.e. BrexitVote,
London, BornUK and UKIP15). For that, your blog post should contain a table with all the variables, the
relevant measure of central tendency (MCT), and the relevant measure of dispersion (MD). The table should
look like this:

BrexitVote · · · · · · · · · · · ·
…
…
…
…
…
Then, you should perform some recodes of the variables of interest, in particular, BornUK and UKIP15. For
each variable, create a corresponding binary variable where 0 corresponds to a value that is equal or below the
median, and 1 corresponds to a value above the median. You should make sure you name the new variables
in an intuitive way (e.g. BornUK_binary)
Once you do that, you should examine the relationship between the brexit vote share and the relevant
variables, For that, you will need to produce – for each theory – the following:
• A justification for the proposed relationship.
• A conditional distribution plot
• A two-sample t-test.
Finally, you should use the plots and the results from the t-tests to write up your blog post. You should
explain whether there is evidence to support your theories and how can they could be improved.
Useful expamples
Here are some useful examples of blog posts that use empirical data and quantitative methods to discuss
political questions. You are welcome to use them as a reference for the writing style and organisation:
1. Profs. Tim Bale and Paul D. Webb on the Labour leadership race
2. Prof. Chris Hanretty on how voters punish (or not) their MPs for the stances they take
3. Profs. Carolina Plescia, André Blais and John Högström on How voters dislike disproportionality
4. Dr. Zac Greene and Dr. Javier Sajuria on Who gets to speak at Labour’s Party Conference
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5. Dr. Nicole Martin and Dr. Scott Blinder on Whether voters are rejudiced or not against ethnic minority
candidates
6. Victoria Donnaloja on National identification and political engagement after naturalisation
7. Neema Begum on How inmmigration policy drives Brexit voters
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