Quantitative Methods in Political Science

Fall 2021

University of Mannheim

Quantitative Methods in Political Science

This course introduces graduate students to quantitative methods in political science. During the first half of the course, we will focus on linear regression models. The topics covered include discussions of the mathematical bases for such models, their estimation and interpretation, model assumptions and techniques for addressing violations of those assumptions, and topics related to model specification and functional forms. During the second half of the course, students will be introduced to the likelihood principle as a theory of inference, including models for binary and count data.

The main goals of this course are to develop sound critical judgment about quantitative studies of political problems, to interpret quantitative analyses in published work, to understand the logic of statistical inference and to recognize and understand basic regression models. It provides the skills necessary to conduct your own quantitative analyses and teaches how to do so using R. This class lays the foundation for Advanced Quantitative Methods, which will be taught in Spring 2022.


Lectures take place on Wednesdays, and corresponding labs are on Thursdays (same week) and Mondays and Tuesdays (following week). Homework assignments are assigned and collected on Tuesdays, and you have one week to complete them. Office hours are online, on Mondays (lab) and Tuesdays (lecture).


Wednesday (Thomas) 8:30-10:00
A5,6 B144


Thursday (Oliver) 10:15-11:45 in A5,6 C-108
Monday (Viktoriia) 12:00-13:30 in A5,6 C-108
Tuesday (David) 17:15-18:45 online

Online Office Hours

Thomas: Tuesday 13:30-14:30
David, Oliver & Viktoriia: Monday 15:30-17:00 &
Thursday 15:30-16:30

Weekly Problem Sets

Assigned after Tuesday lab
Due Tuesday in a week, 23:59
HWs 1-2: HWs 3-10:


Midterm - 50%
Data Essay - 50%
Problem Sets - Pass/Fail


For more details, check out the Syllabus, and Roadmap for the course material overview

Course Material

Here you can find the info you need to prepare for lecture and labs.

Week 12: Count Data

You will work with another commonly used GLM and learn to model discrete positive data.

Week 11: Binary Data

This week you will learn the basics of how to model a binary dependent variable.

Week 10: The Likelihood Theory of Statistical Inference

This week will build the foundation for the second part of the course, and you will get acquainted with a different way to inference …

Week 08: Linear Regression: Diagnostics

In this final chapter on OLS, you will learn more about the assumptions used when doing regression analysis using OLS.

Week 07: Linear Regression: Interpreting Substantive Effects via the Simulation Method

You will learn to present to results of your statistical analysis in an accessible way.

Week 06: Linear Regression: Statistical Inference, Dummies, and Interactions

Here you can find the overview of the week and the readings. This week is all about categorical variables and heterogeneous effects.

Week 05: Linear Regression: Statistical Control & Causality

Here you can find the overview of the week and the readings. We look in more depth into statistical control and you learn how to select …

Week 04: Linear Regression: Basics & Hypothesis Testing

Here you can find the overview of the week and the readings. This week we start with OLS.

Week 03: Sampling and Statistical Inference

Here you can find the overview of the week and the readings. This week is all about uncertainty from sampling and confidence intervals.

Week 02: Fundamentals of Probability

Here you can find the overview of the week and the readings. This week we cover probability distributions, the discrete and continuous …

Week 01: Introduction. Visualizing Data

This week you will learn about the basics of data analysis and visualization. We will talk about the different kinds of variables, how …

Week 00: Getting started

Welcome to the course Quantitative Methods at the University of Mannheim. In this course we will use the open-source software R and …

Getting help in the course

We expect everyone will have questions at some point in the semester, so we encourage you to use the following resources for help.

Ask questions during lectures and labs

If you have a question during lecture or lab, feel free to ask it! There are likely other students with the same question, so by asking you will create a learning opportunity for everyone.

Office hours

The teaching team is here to help you be successful in the course. You are encouraged to attend office hours during the times posted on the home page to ask questions about the course content and assignments. From our experience, we can best help if you come with a specific question in mind.

For time management reasons, if you plan to come to Monday or Thursday (lab) office hours, we ask you to please sign up by 17:00 on the day before (you can use the form below), with at least one specific question you want to have answered. You don’t need to sign up for Tuesday office hours.


Students regularly run into the same issues as one another, so it’s helpful to ask these questions in a shared space. You are thus welcome to post questions in our Slack workspace, in the #help channel. You can ask questions and help each other out by answering questions of your peers. We will monitor the channel and try to respond to your questions there ASAP.


If you have questions about personal matters that are not appropriate for the public space like Slack channels (e.g., illness, accommodations, etc.), please don’t hesitate to email us.

Sign up for Monday and Thursday Office Hours

Course team


Thomas Gschwend



David Grundmanns

Tuesday Lab’s Tutor


Oliver Rittmann

Thursday Lab’s Tutor


Viktoriia Semenova

Monday Lab’s Tutor