Introduction to Generalised Linear Models

Short Bio Federico Vegetti is a post-doc researcher in political science at the Central European University in Budapest. He took his PhD in Political Science from the University of Mannheim in 2013. His research interests include political psychology, comparative political behavior, and quantitative research methodology. Prerequisite knowledge Note from the Academic Convenors to prospective participants: by registering to this course, you certify that you possess the prerequisite knowledge that is requested to be able to follow this course. The instructor will not teach again these prerequisite items. If you doubt whether you possess that knowledge to a sufficient extent, we suggest you contact the instructor before you proceed to your registration. a) Participants should have taken the course on “Multiple Regression Analysis: Estimation and Diagnostics” in the first week of the summer school or have obtained equivalent prior knowledge through other means. b) The course makes use of the freeware statistical package R, which can be downloaded from http://www.r-project.org, and assumes basic skills with the R language (e.g. how to load data and run simple descriptive statistics, but most importantly, the basic logic of “object-oriented” programming). For students unfamiliar with R, a preparatory course will be offered prior to the first week. A good introduction into using R for statistics is “R in a Nutshell – A Desktop Quick Reference” by Joseph Adler (O’Reilly, 2010). c) Students are expected to understand the logic of inferential statistics. Prior knowledge of multivariate statistics is useful but not necessary. Remedial readings: any good statistics textbook, several on-line tutorials. Students familiar with R but in need of a refresher in basic statistics are encouraged to take part in the preparatory course on statistics. d) Furthermore, since a discussion on the use of matrix algebra in regression analysis is scheduled during the first week, it is assumed that the logic of matrix algebra is understood. Short course outline The aim of this course is to offer a detailed but accessible introduction to generalized linear modeling (GLM). Political scientists are often confronted with outcome variables that are not linear, such as e.g. survey respondents' choices among two or more options, which require different types of non-linear transformations in order to be estimated. GLM is a common technique used to obtain meaningful results in these cases. Thus, the aim of the course is to make students comfortable with applying GLM techniques to a variety of outcome variables. The course contains an introduction to the logic of GLM and maximum likelihood estimation, logit models for binary, ordered and multinomial dependent variables, as well as models for count data.