Universität Wien
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230209 SE Advanced Regression Analysis (2010S)

5.00 ECTS (2.00 SWS), SPL 23 - Soziologie
Continuous assessment of course work

Tuesday 14.00-18.00 from 13.4 until 18.5 (incl.), Location: Kursraum A, NIG, EG
Mitteilung der SPL 40:
Die Lehrveranstaltung wird im Doktorat neu - Studienkennzahl 784 - mit nur 3 ECTS bewertet. Bei Rückfragen wenden Sie sich bitte an die zuständige Studienprogrammleitung.

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).

Details

max. 32 participants
Language: English

Lecturers

Classes

Currently no class schedule is known.

Information

Aims, contents and method of the course

This course provides a practical and applied treatment of regression using categorical and limited dependent variables. The most widely used statistical technique, ordinary least squares (OLS) regession, requires a continuous dependent variable. Many of the most interesting social science question, however, have dependent variables that are not continuous: for example, the decision to vote or not. For such variables, different regression techniques need to be used.

By the end of this course, you will be able to construct and interpret regression models for categorical and limited dependent variables, specifically binary, ordinal, nominal and count outcomes. You will have a firm understanding of the assumptions and the interpretation of the model. You will also be aware of the differences between these models and OLS regression; you will be able to use various types of independent variables; you will know how to present the results in a scholarly paper; and you will be able to identify and address possible dangers and problems. You will also be able to evaluate critically such regression models used in scholarly journals.

The course will cover all these models in six sessions. We will begin by reviewing basic statistical concepts, in particular OLS regression, before considering the need for other types of regression models. The next two sessions will consider models for binary outcomes and introduce statistical techniques that can also be used for ordered and nominal outcomes. The final three sessions will cover ordinal, nominal and count outcomes, which share many features of the models for binary outcomes.

Each class will be a mixture of short lectures and computer exercises. Class exercises and homework will be carried out using Stata. This programme will be introduced in the first session, and no prior knowledge of it is required. Training in quantitative methods up to basic OLS regression is required for this course, but detailed knowledge is not expected.

Schedule
Session 1: Introduction to Stata; review of OLS and why we need logit and probit models
Session 2: Regression with binary outcomes (1): logit and probit models
Session 3: Regression with binary outcomes (2): extensions
Session 4: Regression with ordinal outcomes: ordered logit/probit
Session 5: Regression with nominal outcomes: multinomial logit
Session 6: Regression with count outcomes

Assessment and permitted materials

- 10-15 page problem-set paper on main concepts and interpretation of results, assigned after the last class OR 10-15 page paper on main concepts, challenges and interpretation of results based on the perceived topic of the Masters' thesis (50%)
- Four homework and problem sets, to be submitted at four set dates (40%)
- Continuous assessment of class participation (10%)

Minimum requirements and assessment criteria

Examination topics

Reading list

J. Scott Long, Regression Models for Categorical and Limited Dependent Variables, Sage: Thousand Oaks, 1997.
J. Scott Long and Jeremy Freese, Regression Models for Categorical and Dependent Variables using Stata, Stata Press: College Station, 2003. (Newer edition also available.)

Association in the course directory

in 905: MA Methoden oder MA EM

Last modified: Mo 07.09.2020 15:39