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Level
Level: IntroductorySoftware
Member rate £572.50
Non-Member rate £1,145.00
Early bird discount available until 14 December 2026.
Save £45.00 Loyalty discount applied automatically*
Save 5% on each additional course booked
* If you attended a qualifying previous Methods School in 2026, you qualify for £45.00 off your course fee.
Date: Monday 15 – Friday 19 February 2027
Time: 13:30 – 16:30 CET
This interactive course uses advanced pedagogical tools and is designed for master's-level students and above. It runs as three-hour live sessions on Zoom over five days, with course materials and pre-course readings available on our Learning Management System at least two weeks before the start. Office hours are also available for one-to-one consultations on your own project.
The course is limited to 18 participants, ensuring that the teaching team can address the individual needs of each participant.
This course will introduce you to the fundamentals of statistical modelling and data analysis in R for social scientists. You will learn the basics of programming in R, data manipulation, statistical inference, and Ordinary Least Squares (OLS) regression, as well as non-linear regression techniques. We will also cover model building and how to avoid p-hacking. Through the use of various data sets and models, you will gain an understanding of how to use statistical modelling in your own research.
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.
Alexandru Moise is a postdoctoral researcher at the European University Institute (2020–2025). He received his PhD in political science from Central European University in 2019. Alex's research focus is the political economy of welfare reforms. He looks at how individual perceptions and the quality of party linkages affect health care policy. Within the context of the ERC SOLID project, he looks at how crises affect European integration using a variety of quantitative models. He has been teaching quantitative analysis courses at Johns Hopkins University, the European University Institute, Central European University, Ilija University, and the ECPR’s Summer and Winter Schools for many years.
The concept of regression is fundamental to statistical analysis and serves as the foundation for more advanced modelling techniques. Gaining a firm grasp on regression analysis is essential for building a strong understanding of statistical analysis overall.
You will have access to a number of online pre-course materials for you to work through at your own pace. Readings will be supplemented with around four hours of pre-recorded lectures and interactive R notebooks.
You will be provided with an RStudio Cloud account, where you will find all the data and a ready-made set-up for RStudio. With the help of pre-recorded videos, you can start exploring R before the live sessions. You can keep all course materials for future reference.
Pre-recorded lectures will introduce the major topics you will discuss in detail during the course. R notebooks will let you explore R at your own pace, along with discussing the code and models together during the live sessions. Moodle forums will be created for each topic where you can discuss, share code and ask questions.
During the live sessions, you will get to know each other and each other's projects, and explore how you can apply regression analysis to answer relevant questions in political science. The instructor will work with you to tackle the theoretical problems you will face in designing your analysis. They will also help you use R to manipulate data, program models, and to visualise data and results.
You will complete assignments to test the knowledge you have gained. You can discuss these assignments, and any problems you may have, together. The instructor will also host social breaks.
A brief review of the necessary ingredients from probability and statistics. You will learn the basic functionality of the statistical software R through application, starting with the generation of descriptive statistics and graphics.
Beginning with the simple regression model, you will learn about a theoretical derivation of coefficient estimates in the Ordinary Least Squares (OLS) regression model and an overview of its properties. You will also discuss assumptions that underlie the validity of a simple linear regression model.
Once you have established a solid understanding of the simple linear regression model, the instructor will move on to statistical inference.
Covering multiple linear regression, which allows for more than one explanatory variable. Within the context of multiple regression, you will pay particular attention to identifying models that provide the most credible estimate of the explanatory variable of interest.
Non-linear regression models will be introduced along with other more advanced regression techniques.
You must have a basic understanding of probability. Basic knowledge of R would also be useful. If you don't have this knowledge, consider taking the course Introduction to R.
You must complete up to ten hours' preparatory work. This includes:
The course includes three hours of live teaching each day for five days, alongside readings and other learning activities.
Once you have registered and paid, you will receive access to our Learning Management System (LMS) approximately two weeks before the course start date. Course materials, including pre-course readings, will be available here. Allow approximately 20 hours per week before the course to review the materials and complete readings and any pre-course tasks.
During the course week, you should allow approximately two to three hours per day for preparation and assignments, in addition to the live teaching sessions.
The course offers the opportunity to earn three ECTS credits. To earn a fourth credit, you will need to complete a post-course assignment involving approximately 25 hours of work.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach prerequisite items. If in doubt, please contact us before registering.