Exploring Causal Complexity with Qualitative Comparative Analysis (QCA)

About

Workshop Contents and Objectives

Social phenomena rarely have straightforward, single-cause explanations. Instead, outcomes often emerge through intricate combinations of factors, where multiple pathways lead to similar results, and different explanations may be required for success and failure. This interplay of conjunctural causation, equifinality, and causal asymmetry represents the essence of causal complexity - a concept at the heart of Qualitative Comparative Analysis (QCA). Rooted in set theory and the logic of necessary and sufficient conditions, QCA offers a structured approach to understanding these complexities.

This workshop provides participants with a comprehensive introduction to QCA, emphasizing research design and empirical application. Participants will follow an ideal-typical research process, starting with empirical illustrations of how and why QCA is used in the social sciences. These foundational sessions explore key principles, such as set theory, Boolean algebra, and the calibration of crisp and fuzzy sets, while guiding participants through the 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 real-world examples. Advanced topics - including multi-method research, robustness tests, and recent developments in QCA - are tailored to participants’ needs and research interests. Opportunities to present individual projects 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 ensures ample time for consultation, group discussions, and networking. By the end of the workshop, participants 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.

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, participants are not expected to have prior knowledge of QCA nor the R software environment. Preparatory instructions will be circulated ahead of the summer school, so that participants can do some reading, install the relevant software, and familiarize themselves with the environment of R and RStudio. For those new to R it is recommended to take part in the summer school’s preparatory course on R, which takes place in the week prior the workshop.

**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.

Fee info