Applied logistic regression

This course provides "hands-on-experience" to logistic regression analysis. Logistic regression includes methods for discrete dependent variables. Such methods are of vital importance to students of public policy analysis. Many processes that are of interest for public policy analysts are discrete. To give some typical examples: Who votes for right-wing governments? What determines people's opinion on public defence? Who agrees to more government spending for the elderly? In order to answer such questions in a multivariate framework, one needs to draw on logistic regression. Apart from providing an applied introduction to logistic regression modelling, the course also aims at enhancing students' ability to conduct empirical analyses, interpret and visualize findings from multivariate models and deepen STATA-skills. Moreover, students will be introduced to several key micro-level data sets, including census and micro-census data. The course does not require any deep statistical or mathematical knowledge. Students are expected to have interest in answering substantive questions with survey data. However, students should have attended Statistics I (or equivalent) and have some basic knowledge in STATA.
 

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