Final Coursework
Quantitative Data Analysis (POLS0083)
Section 1
Public perceptions of benefit fraud
Trust in social welfare institutions relies on how the public perceives of the deservingness of recipients of such
benefits. In particular, notions of so-called benefit cheats erodes public confidence in social welfare.
Past surveys have shown that the public tend to greatly over-estimate the financial scale of benefit fraud. For
example, a 2013 Ipsos MORI study found that on average, respondents guessed that £24 out of every £100
in benefit claims is done fraudulently, whereas the official estimate is around £0.70 of every £100.
In this section, we explore public perceptions about fraudulently claiming benefits, and in particular, about
how widespread false benefits claims are. More specifically, we will examine whether certain socio-demographic
characteristics are associated with public perceptions about how widely false benefit claims are made in the
UK.
We will use part of the British Social Attitudes (BSA) data set on poverty and wealth, which you can download
as bsa-poverty.csv from the POLS0083 Moodle page. The data set contains the following variables:
Variable name Description
NatFrEst Answer to the question Out of every 100 people receiving benefits in Britain,
how many have broken the law by giving false information to support their
claim?
leftrigh Five-point left-right ideological scale, with 0 to the left and 4 to the right
RSex Sex of respondent, 1 if male or 0 if female
HEdQual3 Completed university degree, 1 if the respondent completed degree or 0 if not
You can load the data set by using the following command:
bsa <- read.csv("data/bsa-poverty.csv")
Questions (42 Marks)
1. How many individuals are included in the data set?
2. Is the left-right ideology similar on average for those who did and not answer the item NatFrEst?
3. Calculate the median of the variable NatFrEst. What does this tell us about the distribution of
perceptions about fraudulent benefits claims?
4. Create a histogram for NatFrEst and interpret it. What does this tell us about public perceptions
about fraudulent benefits claims?
5. We are interested in seeing whether there is a relationship between a persons left-right orientation
and how widespread they think fraudulent benefit claims are. Fit the relevant simple linear regression
model and interpret the substantive significance of the estimated slope coefficient. You do not need to
discuss statistical significance.
6. State a null and an alternative hypothesis for the estimated slope coefficient, decide whether to reject
the null hypothesis, and provide a conclusion.
7. How is your conclusion in Question 6 related to Type I and Type II error?
8. We now add whether the respondent completed a university degree and respondent sex to our analysis.
Interpret the estimated coefficient for the left-right orientation. Does your answer to the question about
the relationship between a persons left-right orientation and NatFrEst change? If so, how?
9. Calculate and interpret the 99% confidence interval for the estimated coefficient for completing a
university degree from the model you fitted in Question 8. What does the estimated standard error tell
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us?
10. Finally, we are interested in whether the relationship between left-right orientation and perceptions of
how widespread benefit fraud is also depends on an individuals education level.
a. Add the relevant interaction term to your regression model and display your results.
b. Assess the model fit for this regression model.
c. Interpret the estimated interaction term and discuss its substantive and statistical significance.
d. Interpret the intercept and its statistical significance. Is the intercept meaningful in this regression
model?
e. Visually represent the results from the regression model using four lines (one each for male
respondents with a university degree, female respondents with a university degree, male respondents
without a university degree and female respondents without a university degree) and describe what
your graph shows.
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Section 2
Dystopian fiction and willingness to justify radical political action
We now look at part of the recent study by Jones and Paris (2018), who conducted a series of survey
experiments to examine whether exposure to dystopian fiction affects an individuals political beliefs. They
define dystopian fiction as portray[ing] a dark and disturbing world dominated by an overwhelmingly
powerful. . . controlling entity that acts to undermine core values. In particular, the authors hypothesised
that exposure to dystopian fiction would lead to higher support for more radical political action, especially
for more violent action.
In the first study, the authors randomly assigned the US-based respondents into two groups. Respondents
assigned to the treatment group first read an excerpt from the first book in Hunger Games, and then watched
a 17-minute video with various violent scenes from the Hunger Games films. Respondents assigned to the
control group were not exposed to any media.
The premise of Hunger Games is that an overwhelmingly powerful government forces individuals to take part
in a contest where the participants are forced to kill each other until only one survivor remains.
The outcome variables are a series of attitudinal items about willingness to justify different disruptive
activities: civil disobedience, damaging government property, cyberattacks on government websites, armed
rebellion, and violent protest.
The data file you will use, which can be downloaded on the POLS0083 Moodle page, is titled dystopia.csv.
The data includes the following variables:
Variable name Description
hgindic Treatment group, with 1 for those exposed to Hunger Games and 0 for the
control group
j_disobed How much civil diobedience can be justified, on a 0-1 scale
j_damage How much damaging government property can be justified, on a 0-1 scale
j_cyber How much cyberattacks on government websites can be justified, on a 0-1
scale
j_rebel How much armed rebellion can be justified, on a 0-1 scale
j_violent How much violent protest can be justified, on a 0-1 scale
female Respondent sex, with 1 as female and 0 as male
ideo Respondent left-right ideological orientation, with higher scores indicating
the right, on a 1-6 scale
You can load the data set by using the following command:
dystopia <- read.csv("data/dystopia.csv")
Questions (33 Marks)
1. What are the proportions of female respondents within the treatment and control groups?
2. We are interested in the outcome variables for civil disobedience and armed rebellion. For each of these
outcome variables:
a. Create a boxplot for each treatment group and interpret the results.
b. Calculate difference in means by treatment group and interpret the results. You do not need to
discuss statistical significance.
c. Conduct the appropriate hypothesis test and interpret the results for the difference in means.
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3. In the current study, in which two ways can we understand the population from which the sample is
drawn?
4. With what assumptions can we interpret the results from Question 2 as causal?
5. Fit a regression model for each outcome variable with the treatment group, sex, and ideology. Does the
estimated difference in means change? What does this tell us about the randomisation of treatment in
this study?
6. Do your results support the authors hypothesis about the link between dystopian fiction and willingness
to support radical political action? State your conclusions in terms of substantial and statistical
significance.
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Section 3
Direct democracy and citizens local support
Does direct democracy (such as local referendums) increase satisfaction with local policies and governmental
institutions? Marien and Kern (2018) explore this question in their article The Winner Takes It All:
Revisiting the Effect of Direct Democracy on Citizens Political Support.
Drawing on the existing literature, the authors argue that direct democratic instruments have short-term
positive effects on satisfaction with local policies and institutions, since they increase the perceived fairness of
decision-making and ability to influence political outcomes among the electorate.
To test their hypothesis, the authors conducted multiple surveys during the spring of 2015, when the
Belgian city Mechelen conducted a referendum on traffic circulation, after citizens had objected to the local
governments initial plans. The researchers collected survey data before and after the referendum took place
both from a sample of local residents in Mechelen who lived in neighbourhoods affected by the traffic diversion
plans (treatment group) and from a sample of residents of a similar neighbourhood in Mechelen not affected
by the traffic circulation plans (control group).
The data file you will use, which can be downloaded on the POLS0083 Moodle page, is titled referendum.csv.
The data includes the following variables:
Variable name Description
idresp Respondent ID
neighb Neighbourhood, 1 as the treatment group and 0 as the control
w1_byear Respondent birth year
primary Highest level of education completed - primary - with 1 Yes and 0 No
secondary Highest level of education completed - secondary - with 1 Yes and 0 No
tertiary Highest level of education completed - tertiary - with 1 Yes and 0 No
w1_trust_general General level of trust on a 0-10 scale, with higher scores meaning more trust,
before the referendum
w1_pol_interest Political interest on a 0-10 scale, with higher scores meaning more interest,
before the referendum
w1_democracy_satis Democratic satisfaction on a 0-10 scale, before the referendum, with higher
scores meaning higher satisfaction
w2_democracy_satis Democratic satisfaction on a 0-10 scale, after the referendum, with higher
scores meaning higher satisfaction
You can load the data set by using the following command:
referendum <- read.csv("data/referendum.csv")
Questions (25 Marks)
1. Is the average level of democratic satisfaction before the referendum significantly different (statistically
and substantively) between respondents in the treatment and control groups?
2. Provide some evidence that the treatment and control neighbourhoods are similar in terms of respondent
characteristics before the referendum took place.
3. Focusing on the respondents living in a neighbourhood affected by the governments plan on traffic
circulation, use a before-after design to estimate the average treatment effect. Is this difference significant
both statistically and substatively?
4. With what assumptions can we consider the results of Question 3 as causal?
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5. Calculate and interpret the difference-in-differences for democratic satisfaction for the two neighbourhoods
and interpret your results.
6. Do the results in Question 5 support the authors hypothesis about the link between direct democracy
and citizens perceptions about democracy? Why or why not?
7. With what assumptions can we consider the results of Question 6 as causal?
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