Course Instructor: Nan Zhang (Office hours by appointment)
Course Website: https://nanzhangresearch.github.io/QMCP
Date and Time: Wednesdays 10:15 - 11:45 in W 114 Seminarraum (Schloss Westflügel)
The course introduces students to data visualization techniques for comparative politics using the R programming language. The goal of this course is to equip the students with the necessary skills to (i) prepare data sets for empirical analysis, (ii) explore and understand patterns and relationships in your data, and (iii) communicate these patterns and findings with effective visualizations.
Attendance and participation: Class time will consist of a mixture of mini-lectures, hands-on practice with R programming, and group discussion / problem-solving.
The R scripts that we will work with during each session are directly downloadable via this website.
Active, in-class participation is central to your learning process. Of course, situations could arise where you need to miss class. As a courtesy, please let me know beforehand if you cannot attend a class session.
Homeworks / student presentations: the R script for each class will contain short exercises for you to complete at home. I will ask a few (randomly selected) students to present their answers to these exercises at the beginning of the following week’s class.
Towards the end of the semester, you will have to complete a take-home exam consisting of a data analysis project.
The take-home exam (and accompanying dataset) will be handed out on the last day of class (22 May).
Your write-up consisting of an HTML report and the underlying R Markdown file should be emailed to me by 30 June 2024. Failure to submit your take-home exam on time will result in failing the course.
I expect you to complete the take-home exam individually. You must make a written declaration that the work is wholly your own when submitting your answers. Simply put: don’t cheat.
Note: the syllabus is a work in progress so some links may not yet work.
Session 1 (14 Feb): Organizational issues and introduction to R
Session 2 (21 Feb): R Markdown and Data Wrangling
Please watch the following video before class: Hans Rosling’s 200 Countries, 200 Years, 4 Minutes
Link to Rmd file for class.
Link to Rmd solutions
Session 3 (28 Feb): More Data Wrangling
Link to Rmd file for class.
Link to settler mortality data from Acemoglu, Johnson and Robinson (2001)
Link to polity4 dataset.
Link to Acemoglu, Naidu, Restrepo, and Robinson dataset for homework.
Link to solutions.Rmd … NOTE: Will not Knit because you don’t have the graphics! You can get an html file with the graphics here.
Session 4 (6 March): Getting to know your data: histograms, density plots and dot plots
Session 5 (13 March): Relationships
Link to Rmd file for class.
Link to simulated experimental data for class.
Link to GLES dataset for your homework.
Link to solutions.
Session 6 (20 March): Making prettier graphs + Annotations
Link to Rmd file with Solutions for class.
Link to Midterm Evaluation survey.
Easter Break
Session 7 (10 April): Regression 1: Coefficients
Session 8 (17 April): Regression 2: Standard Errors
Session 9 (24 April): Regression 3: Confidence Intervals, Hypothesis Testing
1 May: Tag der Arbeit
Session 10 (8 May): Interactions: Subgroup Analyses and Diff-in-Diff
Link to Rmd file for class.
Link to simulated data from Coppock.
Link to solutions
Link to Minimum Wage data for homework.
Session 11 (15 May): Binary Outcomes
Link to Rmd file for class.
Link to corruption data from Andrew Heiss.
Session 12 (22 May): Regression Discontinuity Designs
Link to Rmd file for class.
Link to tutoring program data.
29 May: Break for Corpus Christi / Fronleichnam
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Wickham, H.and G. Grolemund.
This syllabus draws heavily on teaching material developed by David Weyrauch, Verena Kunz, Oliver Rittmann, and David Schweizer. Inspiration is also drawn from courses developed by Andrew Heiss, and materials from Josh McCrain and Nick Jenkins.
Robert Kabacoff’s Modern Data Visualization with R is a great book with lots of examples.
Other sources for teaching materials include:
Acemoglu, Daron, Simon Johnson, and James A. Robinson. “The colonial origins of comparative development: An empirical investigation.” American Economic Review 91.5 (2001): 1369-1401.
Acemoglu, Daron, et al. “Democracy does cause growth.” Journal of Political Economy 127.1 (2019): 47-100.
Coppock, Alexander (2021). “Visualize as You Randomize: Design-Based Statistical Graphs for Randomized Experiments” in Advances in Experimental Political Science. Edited by James Druckman and Donald Green. p.320-339.
Marshall, Monty and Ted Robert Gurr. Polity IV dataset: Center for Systemic Peace.
Teorell, Jan, et al. “The quality of government standard dataset version Jan24” University of Gothenburg: The Quality of Government Institute, (2024).
German Longitudinal Election Study (GLES), 2021. GLES Panel 2016-2021, Wellen 1-15. GESIS Datenarchiv, Köln. ZA6838 Datenfile Version 5.0.0, https://doi.org/10.4232/1.13783.
