Building on the analytical and theoretical background of the previous course in our MA methods sequence (Multivariate Analyses), this course introduces interested graduate students to strategies and tools of how to develop statistical models that are tailored to answer their particular research questions.
You might have noticed by now, the linear regression model is often an inappropriate tool for answering substantive questions in political science. This course serves as an introduction to a multitude of probability models that are appropriate when the linear model is inadequate. After introducing the fundamentals from which statistical models are developed, this course will focus on one specific theory of inference, namely on the statistical theory of maximum likelihood. We will also devote considerable time to statistical programming, simulating and conveying quantities of material interest of such models (using R) in order to encourage students to switch from a consumer-mode into a producer-mode of social science research.
Wednesday 8:30-10:00
A5,6 B244
Thursday 10:15-11:45 in A5,6 B317
Thursday 15:30-17:00 online (until mid-April)
Thomas: Tuesday 13:30-14:30
Oliver: Monday 16:00-17:30
Viktoriia: Tuesday 15:30-17:00
2-3 people
Assigned after lecture
Due next Tuesday, 23:59
Six Homework Assignments - 25%
Final Paper - 75%