Switzerland’s Gun Control Referendum - Estimating Constituency-Level Results from the EU Referendum - Data Analysis Assessment Answer

Data Analysis Assessment Answer

TASK: QUESTION A: Switzerland’s Gun Control Referendum  In Switzerland in 2011, a legally binding referendum was held that would have banned people from keeping guns at home, as well as introducing stricter background checks for those wishing to purchase them. The referendum failed, with 56% of voters opposing it. For this question, suppose that the referendum is going to be repeated next year, and the pro-gun-control campaign asks for your advice. Specifically, the campaign group wants to run an advertising campaign targeted at groups who are most likely to support the new referendum, to persuade them to turn out and vote. Your job is to tell them which types of people are most supportive of gun control. To help measure the likely effectiveness of their advertising, they also want to know how much each characteristic matters in explaining support. You’ll use a survey of voters taken after the first referendum that asked about support for gun control. You need to:

• Choose a logit model that predicts support for gun control, carefully justifying your selection of variables for the model. You must use a minimum of three independent variables.
• Present the model’s findings in ways that clearly explain how much the variables matter in explaining support for gun control

You should present your approach and your findings in the form of a brief report. It should conclude by explaining which types of people you think the campaign should target. The dataset is called “s” and is contained in the file “swiss. Rda”. It contains the following variables for each individual in the survey: QUESTION B: Estimating Constituency-Level Results from the EU Referendum In the 2016 UK referendum on leaving the EU, the results of the vote were not released for individual electoral constituencies. However, many scholars would like to know why people voted to leave the EU, and how support for leaving differed across constituencies. One previous study has already estimated constituency-level support for ‘leave’ in an authoritative way. Your tasks in this question are (i) to produce estimates of the percentage of voters that voted ‘leave’ in every constituency using multilevel modeling and post-stratification that are as close as possible to this existing set of estimates, as measured by the Mean Absolute Error (MAE), and (ii) to use your results to explain why people voted to leave. You need to: i) Estimate an appropriate logistic multilevel model explaining voting for leave, using the predictors in the dataset. 1 ii) Present the multilevel model results and interpret how the variables affect voting to leave the EU (Note: you do not need to discuss statistical significance). iii) Produce post-stratified estimates of the percentage of people who voted ‘leave’ in all 631 constituencies in England, Scotland and Wales iv) Compare your results to the existing estimates using the Mean Absolute Error You should present and explain your approach and results in a brief report, explaining why your estimates do or not perform well compared to the existing estimates. Note: if you cannot get very close to the existing results, do not worry. Your grade depends on the quality of your analysis, presentation, and interpretation, not how close your results are to the existing estimates. The survey data is called “e” and is in the file “eusurvey.Rda”. It comes from 2017 British Election Study and it contains the following variables: QUESTION C: Describing and Classifying Tweets  Many companies monitor social media posts in order to gauge how customers feel about their company and their competitors. For this question, imagine that you have been hired as a consultant by one of the major American airline companies to analyze tweets about airlines. They want to find out how people talk about airlines on Twitter and then build a predictive tool that can classify tweets in future into ‘negative’ or ‘positive’ sentiment toward airlines, to help them respond better to their customers in real time. They have provided you with a dataset of 11,541 tweets about airlines that have been labeled as ‘negative’ or ‘positive’ by their staff. The dataset also identifies which airline each tweet is talking about. Your task is to prepare a brief report that describes the tweets and recommends a classification method for future tweets. You need to: 1. Use appropriate tools to describe the tweets. In particular, what words are associated with negative or positive sentiment? How does word usage differ across different airlines? 2. Use your analysis from (1) to build a short dictionary of negative and positive words describing airlines, then use it to classify tweets as ‘negative’ if they contain more negative than positive language, and ‘positive’ otherwise [code for creating your own dictionary is provided below] 3. Use an appropriate supervised machine-learning method to classify the tweets into ‘negative’ and ‘positive’ 4. Compare the performance of your classifiers from (2) and (3), and use this analysis to decide which one would be the better classifier for the company to use for future tweets Here is some advice for part (2):

•  Your dictionary should contain a minimum of 5 words and a maximum of 15 words in each category
• You are not expected to exhaustively compare the performance of different dictionaries. Instead, simply choose one dictionary based on your analysis from (1), and explain how you chose the words.

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