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ECPR Joint Sessions 2020 Sciences Po Toulouse

WA112 - Maximum Likelihood Estimation

Instructor Details

Instructor Photo

Susumu Shikano

Universität Konstanz

Instructor Bio

Susumu Shikano is Professor of Political Methodology at the University of Konstanz. He is also affiliated with the Mannheim Centre for European Social Research and the Hanse Institute for Advanced Study in Delmenhorst.

His research interests are various topics in electoral politics, coalition formation and methodology.

Susumu has published articles in journals including Public Choice, Political Psychology, Party Politics, West European Politics, and the British Journal of Political Science.


Course Dates and Times

Friday 2 March
13:00–15:00 and 15:30–17:00

Saturday 3 March
09:00–10:30 / 11:00–12:00 and 13:00–14:30

Prerequisite Knowledge

Statistics, including regression models with different types of dependent variables.

Basic knowledge of R.

Short Outline

This course introduces maximum likelihood estimation (MLE), one of the most important methods for parameter estimation in modern social sciences, on which a large body of published empirical works relies.

You will learn the basic idea of MLE, and some of its extensions in several example cases.

In contrast to the least squares methods, MLE requires you to make some assumptions about distributions of stochastic terms. This course therefore also deals with basics of probability theory.

On the final afternoon you will write programs in R to estimate some model parameters.

Long Course Outline

In modern social sciences, statistical analysis has long been established as an inference tool.

Although most published work relies on MLE for its statistical analysis, many researchers seem to be ignorant about MLE per se and rely instead on statistics software packages. Some interpret MLE results as if they were based on the least squares technique. Others cannot distinguish likelihood from probability.

This course, therefore, aims to deepen your understanding of how statistical packages use MLE. This will help you better understand inference, because MLE is closely related to the likelihood model of inference (King 1998).

We begin by discussing some important concepts:

  • likelihood
  • probability
  • uncertainty
  • inference.

Most importantly, we clarify likelihood and probability, and their distinct role in inference. We also look at foundations of probability theories, which many students learn only implicitly in conventional social science statistics lectures. These will help you understand the likelihood-based model of inference.

Using a linear regression example, you will also learn about computation of MLE (maximization algorithms) and their properties in inference statistics, in particular asymptotic ones.

After gaining a conceptual overview of MLE, you will learn how to program R to obtain MLE. R packages are available, so you won't have to program the maximization algorithms yourself, but you will learn how to define the likelihood function.

The course starts with a linear regression model and extends the classes of statistical model to discrete regression models, such as binary logit/probit and poisson models.

We cannot cover a wide range of statistical models in this short course, but after completing it, you should be able to apply the basic concept of MLE and program skills to various statistical models in different practical situations.

Day-to-Day Schedule

Day-to-Day Reading List

Software Requirements

Download R free if you do not already have it installed

Hardware Requirements

Please bring your own laptop


Henning Best and Christof Wolf, eds. The Sage Handbook of Regression Analysis and Causal Inference Sage, 2014; Chapter 2: Martin Elff, Estimation techniques: Ordinary least squares and maximum likelihood

Gary King, Unifying Political Methodology: The Likelihood Theory of Statistical Inference Michigan University Press,1998

The following other ECPR Methods School courses could be useful in combination with this one in a ‘training track .
Recommended Courses Before

Introduction to Statistics
Advanced Topics in Applied Regression
Multiple Regression


Recommended Courses After

Introduction to Bayesian inference
Multilevel Modelling
Panel Data Analysis
Advanced Discrete Choice Modelling


Additional Information


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 in due time.

Note from the Academic Convenors

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, contact the instructor before registering.

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