Is the left-right ideology similar on average for those who did and not answer the item NatFrEst?

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 person’s 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 person’s 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 2 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 individual’s 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. 3 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 individual’s 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. 4 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. 5 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 government’s 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 government’s 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? 6 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? 7