Universität Wien FIND
Warning! The directory is not yet complete and will be amended until the beginning of the term.

400011 SE Regression models for categorical data (2021S)

SE Methods for Doctoral Candidates

Continuous assessment of course work


Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).


max. 15 participants
Language: English


Classes (iCal) - next class is marked with N

Thursday 04.03. 15:00 - 16:30 Digital
Thursday 11.03. 15:00 - 16:30 Digital
Thursday 18.03. 15:00 - 16:30 Digital
Thursday 25.03. 15:00 - 16:30 Digital
Thursday 15.04. 15:00 - 16:30 Digital
Thursday 22.04. 15:00 - 16:30 Digital
Thursday 29.04. 15:00 - 16:30 Digital
Thursday 06.05. 15:00 - 16:30 Digital
Thursday 20.05. 15:00 - 16:30 Digital
Thursday 27.05. 15:00 - 16:30 Digital
Thursday 10.06. 15:00 - 16:30 Digital
Thursday 17.06. 15:00 - 16:30 Digital
Thursday 24.06. 15:00 - 16:30 Digital


Aims, contents and method of the course

This course covers regression models for categorical data. We start by revisiting the linear model and evaluate the linear probability model (LPM) as a first approach to analyzing categorical data. We discuss different estimation techniques and the fundamentals of maximum likelihood estimation (MLE) will be introduced. Next, we explore a broad range of generalized linear models (GLMs) including models for binary, multinomial and ordered outcome variables. We learn how to interpret and visualize their model results by deriving quantities of interest. Finally, we will cover further extensions of these models, such as hierarchical/multilevel models and models for panel data.

Each session will consist of a short lecture followed by practical exercises using a statistical software (R or Stata).

By the end of this course, participants will be able to analyze different types of categorical data using regression techniques widely used in the Social Sciences. They will have a solid understanding of the statistical foundations of these models. They will also be able to interpret those models correctly and apply them to their own work.

Prior knowledge of linear regression and the familiarity with any statistical software will be helpful but are not required in order to complete the course successfully.

Assessment and permitted materials

Minimum requirements and assessment criteria

The final grade will be calculated as the weighted average of the following assignments:
- multiple-choice quizzes (20%),
- class worksheets (20%),
- research outline (20%),
- final paper (40%).

The students should attend at least 80% of the sessions.

Students will be assessed based on their knowledge and understanding of quantitative methods as well as their ability to conduct and write up their independent analysis.

Examination topics

Reading list

Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. New York: Cambridge University Press.
King, G. (1989). Unifying Political Methodology: The Likelihood Theory of Statistical Inference. Ann Arbor: University of Michigan Press.
Long, S. J. (1997). Regression Models for Categorical and Limited Dependent Variables. Advanced Quantitative Techniques in the Social Sciences. Thousand Oaks: Sage Publications.

Association in the course directory

Last modified: Fr 29.01.2021 15:09