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Abstract
Generalized linear models are a class of models for the analysis of multivariate datasets. This class subsumes linear models for quantitative response, binomial models for binary response, loglinear models for categorical data, Poisson models for count data. Models are presented and practised from a problem-oriented perspective.
Objective
The course has a strong focus on the application of GLMs in the social, economic and behavioural sciences. Through the presentation and discussion of case studies and the analysis of a variety of data sets (e.g., demographic, social and economic data) using the software R, students will reflect on 1. the social phenomena and the research questions that can be investigated with GLMs 2. the theoretical and practical considerations that must be taken into account to apply GLMs in a rigorous way. By doing this, students will take away a broader perspective on the standard and unique challenges that the application of GLMs entails.
Content
The following topics will be covered: * Introduction to generalized linear models * The general linear model: ANOVA and ANCOVA * Models for binary outcomes: logistic regression and probit models * Models for nominal outcomes: multinomial logistic regression and related models * Models for ordinal outcomes: ordered logistic regression and probit models * Models for count outcomes: Poisson and negative binomial models
Resources
Lecture Notes
Lecture notes are distributed via the associated course moodle.
Literature
* Fox, John. (2016). Applied regression analysis and generalized linear models (Third ed.). Los Angeles: SAGE. * Fox, John, & Weisberg, Sanford. (2019). An R companion to applied regression (Third ed.). Los Angeles: SAGE. * Hosmer, David W, Lemeshow, Stanley, & Sturdivant, Rodney X. (2013). Applied logistic regression. Hoboken: Wiley. * Long, J. Scott. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks, Calif: Sage Publications.
General Information
- Language
- English
- Levels
- DS , DR
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 40
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture |
Applied Generalized Linear Models
Does not take place this semester.
|
No time listed | 2 h weekly |
Offered In
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Science in Perspective (In “Science in Perspective”-courses students learn to reflect on ETH’s STEM subjects from the perspective of humanities, political and social sciences. Only the courses listed below will be recognized as "Science in Perspective" courses.)
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Type A: Enhancement of Reflection Competence (SiP courses are recommended for bachelor students after their first-year examination and for all master- or doctoral students. All SiP courses are listed in Type A. Courses listed under Type B are only recommendations for enrollment for specific departments.)
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Doctorate Humanities, Social and Political Sciences (More Information at: )