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Midterm Coursework
Introduction to Quantitative Research Methods (PUBL0055)
Instructions
• The coursework will be posted on Moodle on Friday 6th November 2020 at 2pm, and is due on
Wednesday 11th November 2020 at 2pm. Please follow all designated SPP submission guidelines for
online submission as detailed on the PUBL0055 Moodle page. Standard late submission penalties apply.
• This is an assessed piece of coursework (worth 25% of your final module mark) for the PUBL0055
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 or teaching fellows
questions about the coursework.
• Along with the coursework itself, the datasets for the coursework can be found in the PUBL0055 page
on Moodle.
• Coursework should be submitted via the ‘Turnitin Assessment - PUBL0055 - Midterm Assessment’ link
on the course Moodle page. You will need to click the ‘Submit Paper’ link at the bottom of the page.
When presented with the ‘Submit Paper’ box, the ‘Submission Title’ should be your candidate
number, and you should upload your document into the box provided.
– Please remember to state ONLY your candidate number on your coursework (your candidate
number is made up of four letters and one number e.g. ABCD5). Your name and/or student
number MUST NOT appear on your coursework.
• The coursework consists of five questions; you must complete each part of each question to achieve full
marks. Each question is worth 20 marks in total.
• Unless otherwise stated, 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 1000 words. This does not include the appendix.
• Please submit your type-written (numbered) answers 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).
• You may assume the methods you have used (e.g. difference in means, linear regression, etc) are
understood by the reader and do not need definitions, but you do need to explain how they apply to
answering the question.
• Round all numbers to two digits after the decimal point.
• Do not copy and paste any R output (e.g. the output from running lm(y ∼ x)) into your answers.
Create a formatted table that is easy to read.
• All variable names in the coursework are written in this_font.
1
Female Leadership and Public Health Outcomes During COVID-19
Some countries have been more successful than others in terms of public health outcomes during the COVID-19
pandemic. In particular, deaths associated with the virus have been unevenly distributed cross-nationally. Of
the many factors that might be responsible for these differences, a great deal of media attention has focused
on the idea that countries led by female politicians may have been more successful at dealing with COVID-19
than countries led by male politicians. For instance, a recent article in the Guardian newspaper asked “Are
female leaders more successful at managing the coronavirus crisis?” The New York Times went further,
asking “Why are Women-Led Nations Doing Better With COVID-19?” Reporting of this sort relates to
ongoing debates in the academic literature on differences in the efficacy of male and female political leaders.
In this section, you will investigate these ideas empirically by examining the relationship between female
political leadership and COVID-19 death-rates in a sample of countries. This exercise is loosely based on
papers by Purkayastha et al and Coscieme et al, both of which use data on COVID-19 fatalities to estimate
the effects of female leadership on public health outcomes.
The data file you will use, which can be downloaded on the PUBL0055 Moodle page, is titled
covid_country_data.csv and contains information from 180 countries. The data includes the following
variables:
Variable name Description
countryiso3 Unique country identifier
country Country name
deaths_per_100k Total number of deaths recorded from COVID-19 per 100,000 people in the
population
female_leader TRUE if the country has a female leader and FALSE otherwise
ghs_index The Global Health Security index score, a 2019 measure that aimed to predict
how prepared each country was for handling epidemics and pandemics
gdp_percap GDP per capita in current US dollars, measured in 2016
pct_urban The percentage of the population living in urban agglomerations of more than 1
million, measured in 2016
health_gdp_pct Current health expenditure as a percentage of GDP, measured in 2016
democracy TRUE if the country is a democracy and FALSE otherwise (Polity IV,
dichotomised at 6)
continent The continent in which the country is located
You can load the data by using the following command:
covid <- read.csv("data/covid_country_data.csv")
Question 1 (20 marks)
a. Begin your analysis by providing appropriate descriptive statistics on the two main variables of interest
for this analysis, female_leader and deaths_per_100k. Present summaries of both variables, either in
graphical or tabular form. Interpret your results.
b. Calculate the difference in mean deaths for countries with and without female leaders. Interpret this
difference in means in substantive terms. Is this the causal effect of female leadership on public health
outcomes? Why or why not?
Question 2 (20 marks)
a. Estimate two multiple regression models with deaths_per_100k as the dependent variable. For the
first model, include female_leader as the only explanatory variable. For the second model, include
female_leader and three other variables of your choice. Do not include ghs_index. If you decide to include
2
gdp_percap, use a log transformation by including log(gdp_percap) in the model formula. Interpret your
results, making sure to compare and contrast the coefficient on female leadership in the two models.
b. How does controlling for these other variables affect your answer on causality from question 1.b? Describe
two additional variables that are not included here that you might also want to control for to strengthen the
evidence for a causal effect.
Question 3 (20 marks)
In 2019, before the pandemic, researchers constructed an index which was designed to measure how prepared
different countries are for global health emergencies. The scores for each country are stored in the variable
ghs_index.
a. Present descriptive statistics relating to the ghs_index variable and produce a plot to show the relationship
between that variable and deaths_per_100k. Interpret your plot.
b. Use multiple regression to assess whether/how ghs_index is predictive of COVID-19 deaths. Include the
same variables that you included in Question 2 plus ghs_index. Interpret your results.
3
Children’s Television and Educational Performance
Figure 1: Sesame Street
Can educational television programmes improve children’s learning outcomes? Sesame Street is a long-running
American television programme aimed at young children. The creators of Sesame Street decided from the very
beginning of the show’s production that a central goal would to be educate as well as entertain its audience.
In addition to building the show around a carefully constructed educational curriculum, the producers also
worked closely with educational researchers to determine whether the show’s content was effectively improving
its young viewers’ numeracy and literacy skills.
The dataset contained in sesame_experiment.csv includes information on 240 children who were randomly
assigned to two groups. The treatment of interest here is not watching Sesame Street, because it was not
possible to force children to watch or to refrain from watching a freely available TV show. Instead, researchers
randomized whether children were encouraged to watch the show. The parents of children in the treatment
group were encouraged to show Sesame Street to their children on a regular basis, while parents of the
children in the control group were given no such encouragement.
In this section, you will analyse data from this experiment. The data file you will use, which can be
downloaded on the PUBL0055 Moodle page, is titled sesame_experiment.csv and contains information
from 240 children who participated in the experiment. The data includes the following variables:
Variable name Description
encouraged TRUE if the child was encouraged to watch Sesame Street, FALSE otherwise
watched TRUE if the child watched Sesame Street, FALSE otherwise
letters The score of the child on a literacy test (from 0 to 100)
age Age of the child (in months)
female TRUE if the child is female, FALSE otherwise
You can load the data by using the following command:
sesame <- read.csv("data/sesame_experiment.csv")
Question 4 (20 marks)
a. Use the data from the experiment to calculate the following quantities:
1. The proportion of children who were encouraged to watch Sesame Street.
2. The proportion of children who watched Sesame Street.
3. The proportion of children who watched Sesame Street among those who were encouraged to watch.
4. The proportion of children who watched Sesame Street among those who were not encouraged to watch.
What do these figures tell you about the effectiveness of the encouragement?
b. Calculate the difference in mean literacy scores between children who were encouraged to watch Sesame
Street and those who were not. Interpret your results. Does the difference in means estimate the causal effect
of encouraging parents to have their children watch Sesame Street? Why, or why not?
c. Estimate two regression models, both of which should have letters as the outcome variable. In the
first model, include encouraged as the only explanatory variable. In the second model, include encouraged,
female, and age. Interpret the results, making sure that you compare and contrast the results of your models.
Explain any differences or similarities that you observe.
Question 5 (20 marks)
a. Adapt the second model from question 4b to estimate a regression model that allows you to determine
whether the effect of the encouragement depends on the gender of the child. Interpret your results.
b. Discuss the strengths and weaknesses of this experiment for answering the research question posed at the
beginning of this section (“Can educational television programmes improve children’s learning outcomes?”).
What alternative research designs might be used to improve our understanding of the effects of educational
television on child literacy?

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