Universität Wien FIND

400008 SE Regression Models (2018S)

SE Methods for Doctoral Candidates

Continuous assessment of course work


max. 15 participants
Language: English


Classes (iCal) - next class is marked with N

Tuesday 13.03. 11:30 - 15:30 PC-Raum 1 Schenkenstraße 8-10, 1.UG
Tuesday 20.03. 11:30 - 15:30 PC-Raum 1 Schenkenstraße 8-10, 1.UG
Tuesday 10.04. 11:30 - 15:30 PC-Raum 1 Schenkenstraße 8-10, 1.UG
Tuesday 17.04. 11:30 - 15:30 PC-Raum 1 Schenkenstraße 8-10, 1.UG
Tuesday 24.04. 11:30 - 15:30 PC-Raum 1 Schenkenstraße 8-10, 1.UG


Aims, contents and method of the course

This 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.

At the end of this course you will:
- have a solid grounding in theoretical aspects of regression models,
- be able to critically evaluate regression models used in the literature,
- be able to construct and refine a regression-based study design for their own research questions, and
- be able to learn about other regression models through self-study.

Assessment and permitted materials

Assessment criteria:
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.

Minimum requirements and assessment criteria

Examination topics

Reading list

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 31.08.2018 08:43