400010 SE Introduction to regression models (2024S)
Methods seminar
Continuous assessment of course work
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ON-SITE
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
Monday
08.04.
09:00 - 13:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Monday
15.04.
09:00 - 13:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Monday
22.04.
09:00 - 13:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Monday
29.04.
09:00 - 13:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Monday
06.05.
09:00 - 13:00
C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Information
Aims, contents and method of the course
Aims, contents and method of the courseThis course provides a practical and applied introduction to ordinary least squares (OLS) regression models, one of the most widely-used statistical methods in the social sciences. By the end of this course, you will be able to construct and interpret OLS regression models. You will have a firm understanding of the assumptions of the model, the differences between various types of independent variables and how to identify and address possible dangers and problems. You will also be able to evaluate critically OLS models used in scholarly journals. We will begin by reviewing basic statistical concepts, such as comparing means and testing hypotheses, before moving on to the analysis of the association of two continuous variables. We then discuss simple linear regression and the assumptions underlying OLS regression. The final sessions cover the core of this method. First, we examine in detail multiple regression models, concentrating on the practical interpretation of results. Then, different types of explanatory variables are introduced, with a focus on binary/nominal variables and interaction effects. Finally, an overview of possible problems and their remedies is provided, and we will consider how to approach model-building in OLS regression.
Assessment and permitted materials
Minimum requirements and assessment criteria
Assessment and permitted materials
1) Problem-set paper on main concepts and interpretation of results, assigned after the last class OR 10-15 page paper using regression models on a substantive topic related to the PhD thesis (50%).
2) Homework and problem sets after each class, to be submitted at four set dates (40%)
3) Continuous assessment of class participation (10%)
Students need to achieve a pass grade (4) on each of these three assessment criteria. Attendance is mandatory.
1) Problem-set paper on main concepts and interpretation of results, assigned after the last class OR 10-15 page paper using regression models on a substantive topic related to the PhD thesis (50%).
2) Homework and problem sets after each class, to be submitted at four set dates (40%)
3) Continuous assessment of class participation (10%)
Students need to achieve a pass grade (4) on each of these three assessment criteria. Attendance is mandatory.
Examination topics
Reading list
Reading list
Gelman, Hill and Vehtari (2020) Regression and Other Stories, Cambridge UP: Cambridge.
Dougherty, Christopher (2007) Introduction to Econometrics, 3rd edition, Oxford University Press.
Agresti, Alan and Barbara Finlay (2008) Statistical Methods for the Social Sciences, 4th edition, Pearson Education.
Kennedy,Peter (2008) A Guide to Econometrics, 6th edition, Wiley-Blackwell: Oxford.
U. Kohler and U. Kreuter (2012) Data Analysis Using Stata, Third Edition, College Station: Stata Press
Wooldridge, Jeffrey (2009) Introductory Econometrics, 3rd edition, South Western College.
Gelman, Hill and Vehtari (2020) Regression and Other Stories, Cambridge UP: Cambridge.
Dougherty, Christopher (2007) Introduction to Econometrics, 3rd edition, Oxford University Press.
Agresti, Alan and Barbara Finlay (2008) Statistical Methods for the Social Sciences, 4th edition, Pearson Education.
Kennedy,Peter (2008) A Guide to Econometrics, 6th edition, Wiley-Blackwell: Oxford.
U. Kohler and U. Kreuter (2012) Data Analysis Using Stata, Third Edition, College Station: Stata Press
Wooldridge, Jeffrey (2009) Introductory Econometrics, 3rd edition, South Western College.
Association in the course directory
Last modified: Fr 05.04.2024 09:47