ASTK18378U Advanced Quantitative Methods (elective)

Volume 2023/2024

Full-degree students enrolled at the Department of Political Science, UCPH

  • MSc in Political Science
  • MSc in Social Science
  • MSc in Security Risk Management
  • Bachelor in Political Science


Full-degree students enrolled at the Faculty of Social Science, UCPH 

  • Master Programme in Social Data Science
  • Master Programmes in Psychology


The course is open to:

  • Exchange and Guest students from abroad
  • Credit students from Danish Universities
  • Open University students


NOTE Please note that if you register to follow this elective course, you will not be eligible to register for the core-subject track in Political Behaviour (Politisk adfærd) at a later point in your study programme.



This course provides advanced quantitative methods training for students who are not enrolled in the political behavior specialization. It is taught at the same technical level, and provides a broad overview of the key methods for quantitative causal inference as frequently used in the study of political science. Its primary focus is on methods for causal inference: randomized controlled trials, instrumental variables, difference-in-differences, and regression discontinuity.

The course will examine some of the best-known recent quantitative research in political science, and ask: How did the authors produce their results? Can we re-produce them? And how sensitive are the results to other methodological strategies? The course will place particular emphasis on students’ ability to apply the methods introduced in the course to real-world data.

In particular, the course revolves around three sections, as described below.

1. Statistical programming in R

The first classes (classes 1-4) introduce the statistical programming language R, the most widely used statistical software among political scientists who use advanced quantitative methods. Like Python, R’s data science counterpart, R is also widely used in government and the private sector for data analysis. For applied academic, government, and private-sector research, knowing R (and Python) will likely be the most valuable technical tools you learn from your time in university. Accordingly, you will use R in every class throughout the term. You will learn R in class, and also through exercises on DataCamp, an intuitive platform for learning a variety of statistical programming languages and methods.


The first section of the course will also re-introduce linear regression analysis (OLS), which you should be familiar with from your undergraduate methods sequence, and its implementation in R. Finally, I will introduce you to LaTeX, a type-setting system used by the majority of researchers who do advanced quantitative research in political science, and which may be very useful when you write your assignments and, later, your Master’s thesis.


2. Causal inference

The second section of the course will focus on causal inference (classes 5-11), and recent methods developed for estimating causal effects. The social sciences (particularly economics and political science) have recently undergone a “credibility revolution” that places methods for estimating causal effects at the center of research design. This is because theoretically driven empirical research often centers on questions of a causal nature. As a result, much of the course will emphasize how to think about causality conceptually, and the statistical techniques for causal inference.


3. Data collection

The third section of the course will focus on data collection and the analysis of unstructured data (classes 12-14). The variety and scale of data have increased immensely during the past decade. This is primarily due to the growth in social media and the vast arrays of online texts and other forms of online data. In the last three classes of the term, we will therefore examine tools for scraping data, using application programming interfaces (APIs) (e.g. the Twitter API), and analyzing the resulting data. Depending on the time available, I will also provide an introduction to text analysis and machine learning.

Learning Outcome


After completing the course, the student is expected to be able to:

  • Identify relevant designs and techniques for answering research questions in quantitative political science
  • Demonstrate knowledge of how each of the primary methods for causal inference work and why



After completing the course, the student is expected to be able to:

  • Process data in structured and unstructured formats for subsequent analysis
  • Apply quantitative methods for causal inference in statistical software



After completing the course, the student is expected to be able to:

  • Analyze empirical questions in political science using methods for causal inference
  • Criticize contemporary applications of research designs for causal inference in existing academic research


  • Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, Analysis, and Interpretation. WW Norton.
  • Angrist, J. D., & Pischke, J. S. (2014). Mastering ’metrics: The Path from Cause to Effect. Princeton University Press.



Various articles will be included in the weekly readings to supplement the main texts, and will be posted on the course website at: https:/​/​​ResearchMethodsCourse/​

Passed Methodology 2/3, DAK2, and DAK3 or equivalent
Each class will be spent with one hour of lecture and one hour of hands-on application in statistical software.

The lecture will be structured around introducing one of the major methods in research designs for causal inference; seeking to provide intuition for how it works and the assumptions that underlie it; and providing a demonstration of how to apply it in software.

The hands-on application will be structured around an exercise in which students will be asked to apply the method as taught in the first hour of class.
  • Category
  • Hours
  • Class Instruction
  • 28
  • Total
  • 28

Students will receive feedback in three ways:


  • In-person help and feedback during office hours and email
  • Online video-based feedback for the first assignment, which asks students to demonstrate their competency in the statistical software R
  • Written feedback for the second assignment, which asks students to replicate or reproduce an existing quantitative study that is designed to estimate a causal effect
7,5 ECTS
Type of assessment
Written examination
Type of assessment details
Portfolio exam
Marking scale
7-point grading scale
Censorship form
No external censorship

- In the semester where the course takes place: Free written assignment

- In subsequent semesters: Free written assignment

Criteria for exam assesment
  • Grade 12 is given for an outstanding performance: the student lives up to the course's goal description in an independent and convincing manner with no or few and minor shortcomings
  • Grade 7 is given for a good performance: the student is confidently able to live up to the goal description, albeit with several shortcomings
  • Grade 02 is given for an adequate performance: the minimum acceptable performance in which the student is only able to live up to the goal description in an insecure and incomplete manner