Homework 3
Analyzing Democracy
In this homework assignment, we are once again going to be exploring the correlates of democracy with V-Dem. But this time we are going to do so in the context of regression analysis.
Step 1: Load the Data (20 pts.)
Select the measure of democracy that you want to use and at least three variables that you believe correlate with democracy. Use the vdem or vdemlite package to load the V-Dem data into a data frame in your environment.
If there is another outcome in the V-Dem dataset besides a measure of democracy that you would want to analyze, feel free! the point of this exercise is mainly to practice running regression models and the exact variables that you use as independent and dependent variables are not as important.
Step 2: Run a Bivariate Linear Regression Model (20 pts.)
Run a bivariate linear regression model with your selected measure of democracy as the dependent variable and one of your selected predictors as the independent variable. Use the lm() function to run the model and the summary() function to view the results.
Step 3: Interpret the Results (20 pts.)
Interpret the results of the regression model. What is the coefficient on the predictor variable? What does this coefficient tell you about the relationship between the predictor and democracy? Is the relationship stastically significant? How about the constant (intercept term)?
Step 4: Run a Multiple Linear Regression Model (20 pts.)
Run a multiple linear regression model with your selected measure of democracy as the dependent variable and at least three of your selected predictors as the independent variables. Use the lm() function to run the model and the summary() function to view the results.
Step 5: Interpret the Results (20 pts.)
Interpret the results of the multiple regression model. What is the coefficient on each of the predictor variables? What does this coefficient tell you about the relationship between the predictors and democracy? Are the relationships statistically significant? How would you interpret the constant (intercept term)?
Step 6 (extra credit): Visualize the Results (5 pts.)
Use the modelplot() function from the modelsummary() package to create a coefficient plot or regression table visualizing your results. If you choose a regression table, you’ll probably want to run a few more models to get a more complete table.
While step six is extra credit, you will have to do it for the final project, so may as well get some practice now!
Head over to Blackboard and go to the Homework 3 assignment. There you will find a link that you can use to upload your project folder.
If you worked with a partner, only one of you should submit the project folder but both of you should still make a submission to Blackboard so that we have a submission to grade. If you have worked with someoone but still have a substantially different project, you can submit separately.
The person submitting the project folder should take a screen shot of the confirmation message (“Finished Uploading”) and share it with the other group members. This screen shot will be what you submit in Blackboard.
Next, click “Create Submission” and write a statement stating whether you worked on the project independently or whether you are submitting jointly with someone else. Also note whether you used a large language model (LLM) like ChatGPT to help you with the work and which parts of the assignment you used the LLM for. It is fine to use an LLM to help with your code but you must acknowledge the use of it in your submission.
Then, upload the screenshot you saved in the first step. Now submit the assignment.
Please remember to upload your entire project folder and not just the Quarto document.