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Discover ECPR's Latest Methods Course Offerings

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Regression Refresher (before you take a more advanced stats course)

Member rate £492.50
Non-Member rate £985.00

Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked

*If you attended our Methods School in July/August 2023 or February 2024.

Course Dates and Times

Friday 22 February 13:00–15:00 and 15:30–18:00

Saturday 23 February 09:00–12:30 and 14:00–17:30

Levente Littvay

Central European University

So, you know regression. (If you don’t, this class is not for you. I recommend taking the week-long regression analysis class.) You run regression models in your work regularly, but you have not stopped to think about them lately. Once upon a time you may have learned (or even cared) about the assumptions of the models. You may have thought often about what could go wrong.

Then you got into a work flow that simply runs the models and you worry that

  1. you may be making mistakes and/or
  2. you need more advanced techniques but maybe do not have the necessary foundations to venture into those topics.

If that's the case, this course was designed for you.

If you have not thought about linearity, collinearity or heteroscedasticity beyond coming to the realisation that you should look into these issues but you do not; if you never heard of these things and you are regularly running regressions like you know what you are doing, please take this class. It will be good for you.

The secondary goal of this class is to review the foundations you may not have thought much about lately. This could be crucial when taking more advanced courses where the instructor will probably assume you know the problems associated with regressions and want to solve them, typically

  • autocorrelation in multilevel regression
  • panel data and time series
  • measurement error in structural equations
  • limited dependent variables in logistic regression and general linear models, etc
Tasks for ECTS Credits

1 credit (pass/fail grade). Attend at least 90% of course hours, participate fully in in-class activities, and carry out the necessary reading and/or other work prior to, and after, class.

Instructor Bio

Levente Littvay researches survey and quantitative methodology, twin and family studies and the psychology of radicalism and populism.

He is an award-winning teacher of graduate courses in applied statistics with a topical emphasis in electoral politics, voting behaviour, political psychology and American politics.

He is one of the Academic Convenors of ECPR’s Methods School, and is Associate Editor of Twin Research and Human Genetics and head of the survey team at Team Populism.


This course is a refresher about regression. This means that I assume you know regression. You may just need more context, more in depth knowledge, maybe just a review before you take a more advanced class. This course is not for you if you do not know regressions. It is not the place to learn it. (We have a week-long regression course for that.) If you think it will be enough to take this quick course and, without much practice, move to a more advanced, class, please think again. You may not get much out of such a combo.

On the first day, we cover the conceptual foundations of statistical inference. Then we launch into the math underlying regression (in the most user-friendly way humanly possible – do not worry). Then we turn to regression’s multivariate form focused mainly on the functioning of ordinary least squares. We finish the day (or if we take too long, start the next day) with a review the assumptions of regression models both from the perspective of why these assumptions exist and what happens if you violate them. We cover practical tips on how to deal with assumption violations. We start by discussing model specification both from the perspective of omitted variable bias and the inclusion of unnecessary variables. We discuss measurement scales and measurement errors of the variables in the model. We cover homoscedasticity, autocorrelation, linearity of relationships and collinearity. Though not assumptions strictly speaking, we also go into good practices that prevent assumption violations like the treatment of outliers and diagnosis of all variable distributions. We cover these topics both in theory and (on Saturday) through an example (presented in R, though no knowledge of R is assumed).

You will have no use for a computer on the first day. You may be able to follow along on a computer if you have a background in R, on Saturday, but the emphasis will be less on learning how to run things and more on looking at the results of a real-world example. In this example, we place a strong emphasis on interpretation of model results and understanding the difference between bivariate and multivariate answers to the same questions. We finish the discussion of linear regressions by looking at interactions. Examples presented come from day-to-day life, not necessarily some niche of political science, and therefore generalise well to whatever application you want to use.

At the end of the course we briefly venture into the world of logistic regression and general linear models, both in theory and in practice (using R). We put a strong focus on intuitive interpretations offered by logistic regression models. (Or in other words, how can you translate your results into language even your grandmother will understand?)

We finish with a quick overview of where you can go from the point at which this class finishes. What else lies beyond in the world of statistical analysis?


This is a refresher course. I expect you to know regression and the basics of inferential statistics (hypothesis testing, central limit theorem, t test, correlations).

But, maybe, you have not thought about these issues or regression that much lately. Maybe you got comfortable running those models without thinking sufficiently hard (as you were told, once upon a time hopefully) about what is going on in the background, if you are violating some assumptions or, in general, what all could go wrong with your inference.

If you are one of these people and ready to venture into a more advanced statistical topic (panel data, time series, structural equations, multilevel modelling) and you need a refresher, this is the class for you.

Day Topic Details
Friday afternoon: 13:00-15:00 and 15:30-18:00 = 4.5 hours Review of Basics

We review the basics of statistical inference, the mathematical foundations and assumptions of OLS regressions.

Saturday morning: 9:00-10:30 & 11:00-12:30 = 3 hours Application

We look at an applied example in R with a focus on interpretation, testing the assumptions and understanding interactions.

Saturday afternoon: 14:00-15:30 & 16:00-17:30 = 3 hours Quick review of Logistic Regression and what lies beyond

We introduce logistic regression, go through an example in R with a focus on how to make interpretation of the results intuitive. Finally, we look beyond regression in a brief overview of what else is out there once you got this far.

Day Readings
Friday afternoon

Gravetter and Wallnau Statistics for the behavioral sciences Ch.16

Lewis-Beck and Lewis-Beck Applied regression: An introduction (2015)

Saturday morning

Brambor et al (2005)

Saturday afternoon

Pampel (2000)

Software Requirements

I will present a few examples in a recent version of R. No need to follow along in R in the class.

If R is what you would like to learn, take one of the three following short courses:

Automated Web Data Collection with R

Introduction to R (entry level)

Introduction to R (for participants with some prior knowledge in command-line programming)

Hardware Requirements

No computer needed in class. I will show some software examples but the purpose is not to follow the examples together.


Gravetter, Frederick J, and Larry B. Wallnau Statistics for the behavioral sciences Cengage Learning, 2016. Chapter 16

Lewis-Beck, Colin, and Michael Lewis-Beck Applied regression: An introduction Vol. 22. SAGE, 2015

Fox, John Regression diagnostics: An introduction Vol. 79. SAGE, 1991

Brambor, Thomas, William Roberts Clark, Matt Golder (2005), “Understanding Interaction Models: Improving Empirical Analyses”, Political Analysis 13:1-20.

Pampel, Fred. Logistic Regression: A Primer. Vol. 132. SAGE, 2000.

Reference text / additional reading

Fox, John Applied regression analysis and generalized linear models SAGE, 2015


Recommended Courses to Cover Before this One

Summer and Winter School

  • Introduction to Statistics
  • Introduction to Regression (not if you have taken it recently)

Recommended Courses to Cover After this One

Summer and Winter School

  • Logistic Regression / General Linear Model
  • Panel Data Analysis
  • Time Series Analysis
  • Multilevel Modelling
  • Structural Equation Modelling