Exploring Causal Complexity with Qualitative Comparative Analysis (QCA)

About

Workshop Contents and Objectives

Causal complexity is everywhere in the social sciences—yet most researchers lack the systematic tools to capture it rigorously. In this intensive 5-day workshop (28 contact hours) you will learn how to design, apply, and publish empirical research with Qualitative Comparative Analysis (QCA) in the R software environment. The course follows the comprehensive framework developed in Mello (2021, Georgetown University Press), progressing systematically from the foundations of QCA to advanced applications.

The course emphasizes research design alongside analytical techniques, addressing both conceptual foundations and practical application of QCA. You will follow an ideal-typical research process, starting with empirical illustrations of where and how QCA is used in the social sciences. Foundational sessions explore key principles, such as set theory, Boolean algebra, and the calibration of crisp and fuzzy sets, while guiding you through the analytical protocol for identifying patterns of causal complexity using truth tables and Boolean minimization.

The course progresses step by step, from study design to the interpretation of results, incorporating hands-on exercises with examples from published studies and R script templates to adapt for your own purposes. Advanced topics—including multi-method research, robustness tests, and recent developments in QCA—will be tailored to your needs and research interests. Opportunities to present your individual project and explore potential applications further enhance the workshop.

Designed to be inclusive, the workshop welcomes participants at all levels—from PhD students to senior researchers—and strikes a balance between theory, practical exercises, and individualized support. A strong emphasis on collaboration and dialogue in a small group setting ensures ample time for consultation, group discussions, and networking. By the end of the workshop, you will be equipped with the theoretical knowledge and practical skills needed to apply QCA effectively, providing a robust framework for addressing causal complexity in comparative social science research.

Workshop design

Lectures, exercises, participant presentations, individual consultations.

Detailed lecture plan (daily schedule)

A detailed and updated lecture plan will be circulated before the workshop.

Class materials

Presentation slides, R scripts, sample data sets, exercises, published examples.

**The Summer School cannot grant credits. We only deliver a Certificate of Participation, i.e. we certify your attendance.**

If you consider using Summer School workshops to obtain credits (ECTS), you will have to investigate at your home institution (contact the person/institute responsible for your degree) to find out whether they recognise the Summer School, how many credits can be earned from a workshop/course with roughly 35 hours of teaching, no graded work, and no exams.

Make sure to investigate this matter before registering if this is important to you.

Course leader

Patrick A. Mello is an Assistant Professor of International Security at the Department of Political Science and Public Administration of the Vrije Universiteit Amsterdam.

Target group

doctoral researchers, early career researchers, experienced researchers

Prerequisites

While beneficial to gain the most from the course, you are not expected to have prior knowledge of QCA nor the R software environment. Instructions will be circulated ahead of the summer school, so that you can read into the topic, install the relevant software, and familiarize yourself with R. For those new to R it is recommended to take part in the summer school’s preparatory course.

Fee info