Course co-directors: Levente Littvay (CEU Democracy Institute) and Inna Melnykovska (EUI)
This course is organized into three independent modules, each offering a focused engagement with a distinct methodological area. Students may choose to enroll in any single module or combine multiple modules depending on their interests and research needs. Module 1. “Qualitative Data Analysis” is a practical module that introduces students to key methods for analyzing qualitative data. It explores how meaning is generated from texts, interviews, and other qualitative materials through systematic coding and interpretation. The module covers coding approaches, qualitative content and thematic analysis, and discourse analysis, with a strong emphasis on hands-on practice using real samples and examples. Module 2. “Process Tracing: Research Design and Practice” offers a comprehensive introduction to process tracing, the leading method for within-case research, which enables an in-depth examination of the causal mechanisms linking a cause (X) to an outcome (Y). Students will begin by exploring the epistemological and methodological foundations of process tracing, understanding how it approaches causality in comparison to other quantitative (large N) and qualitative (small N) methods. The course places a strong emphasis on practical application. Module 3. “Statistical Analysis Basics in the Age of AI” will cover the basics, minimizing (though not entirely eliminating) math and focusing on the concepts necessary to build a strong basis in quantitative social science research. Artificial intelligence today allows us to demonstrate statistical concepts in new ways, unlike the days of blackboard math or the steep learning curves of statistical software. We are going to take advantage of AI technological developments to approach the basics of statistics (sampling, hypothesis testing, correlation, regression) from a different, more intuitive angle, helping people learn or solidify the much-needed statistical foundations they often struggle with.