Course Instructor: Nan Zhang (Office hours by appointment)
Course Website: https://nanzhangresearch.github.io/QMIR
Date and Time: Fridays 10:15 - 11:45 in A 104 Seminarraum (B 6, 23-25 Bauteil A)
This course will teach students how to address social science questions in the fields of international relations and European integration by analyzing quantitative data in R. The course will introduce students to R, a free and versatile software environment for statistical computing and graphics. Students will learn about data management, basic principles for statistical inference, techniques for dealing with continuous and binary dependent variables and data visualization.
After this course, you will be able to:
The overarching goal is to provide students with the foundation to perform their own analyses (e.g. for their BA theses) by transferring the acquired skills to their research interests.
Required readings: Each week’s readings are directly linked in the syllabus. You are expected to complete the required readings before class.
Attendance and participation: Classtime will consist of a mixture of mini-lectures, hands-on practice with R programming, and group discussion / problem-solving.
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.
learnr tutorials: learnr is a programme that allows you to revise each week’s course materials interactively and at your own pace. These will be available starting in Week 3, and should be completed by 8:00 Wednesday before the following session. Note that late submissions will not be accepted.
Successful completion of 8 out of 10 tutorials is required to pass this coursework.
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 17 June 2024.
Your write-up consisting of an HTML report and the underlying R Markdown file must be submitted by 24 June 2024. Please email me your write-up directly (no need to upload to Ilias). 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.
Session 1 (16 Feb): Organizational issues and introduction to R
Session 2 (23 Feb): R Markdown
Link to lab Rmd for class.
Link to UN votes dataset for class.
Link to lab solutions
Session 3 (1 March): Data Wrangling 1
Link to lab Rmd for class.
Link to Parlgov elections dataset for class.
Link to learnr installation guide
Link to lab solutions
Session 4 (8 March): Data Wrangling 2
Link to lab Rmd for class.
Link to ESS dataset for class.
Link to WB dataset for class.
Link to lab solutions.
Session 5 (15 March): Data Visualization
Link to lab Rmd for class.
Link to Brexit dataset for class.
Link to solutions.
Session 6 (22 March): Bivariate Linear Regression Analysis
Reading: ModernDive: chapter 5
Link to lab Rmd for class.
Link to Opposition dataset.
Link to lab solutions.
Please take a moment to fill in the midterm evaluation.
Easter Break
Session 7 (12 April): Multiple Linear Regression Analysis
Reading: ModernDive: chapter 6
Link to lab Rmd for class.
Link to lab solutions
Session 8 (19 April): Uncertainty and Inference
Session 9 (26 April): Interaction Models
Reading: Brambor, T., W. Clark and M. Golder. (2006). “Understanding Interaction Models: Improving Empirical Analyses”. Political Analysis 14(1), 63-83.
Link to lab Rmd for class.
Link to lab solutions
Session 10 (3 May): Time-Series Cross-Section Regression Analysis
Reading: Hanck, C., Arnold, M., Gerber, A., and Schmelzer, M. Introduction to Econometrics with R. Chapter 10 “Regression with Panel Data”
Link to lab Rmd for class.
Break for Ascension / Christi Himmelfahrt
Nan will hold (virtual) office hours during regular class time if you have questions
Session 11 (17 May): Difference-in-Differences
Reading: Huntington-Klein, N. The Effect: An Introduction to Research Design and Causality. chapter 18.1 and 18.2.1
Link to lab Rmd for class.
Session 12 (24 May): Logistic regression
Reading: Pollock, P. and B. Edwards. (2019). “Logistic Regression” in The Essentials of Political Analysis, 6th ed., London, UK: Sage, pp. 279-306.
Reading: Hanck, C., Arnold, M., Gerber, A., and Schmelzer, M. Introduction to Econometrics with R. Chapter 11 “Regression with a Binary Dependent Variable”
Link to lab Rmd for class.
Break for Corpus Christi / Fronleichnam
Nan will hold (virtual) office hours during regular class time if you have questions
This syllabus draws heavily on teaching material developed by David Weyrauch, Verena Kunz, and Oliver Rittmann. Inspiration is also drawn from courses developed by Andrew Heiss.
Selected readings are from:
R4DS: Wickham, H.and G. Grolemund. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
ModernDive: Ismay, C. and A.Y. Kim. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse