Universität Wien
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400016 SE Regression models for categorical data (2022S)

5.00 ECTS (2.00 SWS), SPL 40 - Doktoratsstudium Sozialwissenschaften
Prüfungsimmanente Lehrveranstaltung
Moodle

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

  • Dienstag 08.03. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 15.03. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 22.03. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 29.03. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 05.04. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 26.04. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 03.05. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 10.05. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 17.05. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 24.05. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 31.05. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 14.06. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 21.06. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
  • Dienstag 28.06. 16:45 - 18:15 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This course covers regression models for categorical data. We start by revisiting the linear model and evalu-ate 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, multino-mial 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 hierar-chical/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 regres-sion 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

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%).

Mindestanforderungen und Beurteilungsmaßstab

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.

87-100 points: Very good (1)
75-86 points: Good (2)
63-74 points: Satisfactory (3)
50-62 points: Sufficient (4)
0-49 points: Not sufficient (5)

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 Quantita-tive Techniques in the Social Sciences. Thousand Oaks: Sage Publications.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Di 08.03.2022 15:09