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400014 SE Advanced topics in regression models (2024S)
Vertiefungsseminar Methoden
Continuous assessment of course work
Labels
requirement dissertation agreement
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Th 01.02.2024 09:00 to Su 25.02.2024 23:59
- Deregistration possible until Mo 18.03.2024 23:59
Details
max. 15 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 06.03. 13:15 - 16:30 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
- Wednesday 20.03. 13:15 - 16:30 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
- Wednesday 10.04. 13:15 - 16:30 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
- Wednesday 15.05. 13:15 - 16:30 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
- Wednesday 29.05. 13:15 - 16:30 Seminarraum 19, Kolingasse 14-16, OG02
- Wednesday 12.06. 13:15 - 16:30 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
- Wednesday 26.06. 13:15 - 16:30 Seminarraum 19, Kolingasse 14-16, OG02
Information
Aims, contents and method of the course
This course covers advanced topics in regression models. We start by revisiting the linear model and evaluating the linear probability model (LPM) as an approach to analyzing categorical data. We discuss different estimation techniques and introduce the fundamentals of maximum likelihood estimation (MLE). We then explore a broad range of generalized linear models (GLMs), including models for binary, multinomial, ordered, and count data. We practice presenting and visualizing model results using post-estimation techniques, which is particularly useful for interpreting interaction effects in non-linear models. Finally, we cover further extensions of regression models, such as hierarchical/multilevel models and models for panel data. We also address frequently asked questions in the context of regression models, such as when to cluster standard errors and when to use fixed- or random-effects specifications.Each session will consist of a short lecture followed by practical exercises using either R or Stata statistical software.By the end of this course, participants will be able to analyze different types of data using regression models, which are widely used in the Social Sciences. They will have a solid understanding of the statistical foundations of these models. They will be able to interpret regression models correctly, visualize model results in an appealing way, and apply regression techniques to their own research problems.Prior knowledge of linear regression and familiarity with any kind of statistical software are helpful but not strictly required in order to participate and complete the course successfully.Please bring a laptop for the practical exercises, if possible, as there are no computers in the room on-site. Alternatively, you can also participate remotely online.Please note: The prerequisite for participation in advanced seminars is the conclusion of the doctoral thesis agreement.Bitte beachten Sie: Voraussetzung für den Besuch von Vertiefungsseminaren ist der Abschluss der Dissertationsvereinbarung.
Assessment and permitted materials
The final grade will be calculated as the weighted average of the following assignments:- reading quizzes (20%),
- exercises (20%),
- research outline (20%),
- seminar paper (40%).The seminar paper can be submitted in English or German.
- exercises (20%),
- research outline (20%),
- seminar paper (40%).The seminar paper can be submitted in English or German.
Minimum requirements and assessment criteria
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 their own research.Grading scale:
- 100-87 Points: Excellent (1)
- 86-75 Points: Good (2)
- 74-63 Points: Satisfactory (3)
- 62-50 Points: Sufficient (4)
- 49-0 Points: Insufficient (5)
- 100-87 Points: Excellent (1)
- 86-75 Points: Good (2)
- 74-63 Points: Satisfactory (3)
- 62-50 Points: Sufficient (4)
- 49-0 Points: Insufficient (5)
Examination topics
Reading list
Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M. (2017). When should you adjust standard errors for clustering?. The Quarterly Journal of Economics, 138(1), 1-35.
Best, H., & Wolf, C. (Eds.). (2013). The SAGE Handbook of Regression Analysis and Causal Inference. Sage.
Clark, T. S., & Linzer, D. A. (2015). Should I use fixed or random effects?. Political science research and methods, 3(2), 399-408.
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.
Hanmer, M. J., & Ozan Kalkan, K. (2013). Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models. American Journal of Political Science, 57(1), 263-277.
Best, H., & Wolf, C. (Eds.). (2013). The SAGE Handbook of Regression Analysis and Causal Inference. Sage.
Clark, T. S., & Linzer, D. A. (2015). Should I use fixed or random effects?. Political science research and methods, 3(2), 399-408.
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.
Hanmer, M. J., & Ozan Kalkan, K. (2013). Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models. American Journal of Political Science, 57(1), 263-277.
Association in the course directory
Last modified: We 31.07.2024 12:06