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400011 SE Regression models for categorical data (2021S)

Seminar für DissertantInnen: Methoden

Prüfungsimmanente Lehrveranstaltung

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 15 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Mindestanforderungen und Beurteilungsmaßstab

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.

Prüfungsstoff

Literatur

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.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 29.01.2021 15:09