K0SP 2025 - QUANTITATIVE METHODS II

This is an intermediate level course of quantitative methods for social scientists. It builds on Quantitative methods I, in which students learn descriptive statistics, the logic of inference, and OLS regression analysis, as well as basic programming in R. This course, aims to extend this knowledge by introducing the logic of maximum likelihood estimation. The course then applies MLE to a broad spectrum of practical questions, particularly to the analysis of categorical or ordinal dependent variables, time-series analysis, multi-level modeling, and data reduction techniques. The course further extends students' knowledge of R programming with a focus on data management

and graphing. The course is based on a mixture of lectures and practical application sessions. Students will thus be expected to bring their computers with R to the

meetings and be ready to work on practical problem sets in most classes. Besides these problem sets and a midterm exam, the students will write a _nal project applying the methods learned in class to a research topic of their interest.

Lecture and lab

Activities 40% (continuous throughout the semester)

Final project 60%