Install the app
Install this application on your home screen for quick and easy access when you’re on the go.
Just tap then “Add to Home Screen”
Install this application on your home screen for quick and easy access when you’re on the go.
Just tap then “Add to Home Screen”
Menu
Install this application on your home screen for quick and easy access when you’re on the go.
Just tap then “Add to Home Screen”
Monday 7 ꟷ Friday 11 February 2022
2 hours of live teaching per day
15:30 ꟷ 17:15 CET
VIR: This is a virtual course
This course provides a highly interactive online teaching and learning environment, using state of the art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team can cater to the specific needs of each individual.
This course introduces you to set-theoretic methods and their application in the social sciences, with a focus on Qualitative Comparative Analysis.
It starts by familiarising you with the basic concepts of the underlying methodological perspective, among them the central notions of necessity and sufficiency, formal logic and Boolean algebra. From there, we move to the logic and analysis of truth tables and discuss the most important problems that emerge when this analytical tool is used for exploring social science data.
Right from the beginning, you will perform set-theoretic analyses with the relevant R software packages.
When discussing set-theoretic methods, our debates will engage on broad, general comparative social research issues, such as case selection principles, concept formation, questions of data aggregation and the treatment of causally relevant notions of time.
We will draw examples from published applications in the social sciences.
3 credits Engage fully with class activities
4 credits Complete a post-class assignment
Nena (Ioana-Elena) Oana is a Research Fellow at the European University Institute, Florence, where she is currently working on developing semi-automated solutions for protest event analysis in the framework of the SOLID project.
Nena is the main developer of the R package SetMethods and has extensive experience in teaching QCA using R at various international methods schools and universities (ECPR Methods Schools, Lund University, University of Helsinki, EUI, etc).
She has also co-authored, with Carsten Q. Schneider and Eva Thomann, the book Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide, forthcoming with Cambridge University Press.
Besides research methodology, Nena's main research interests include political participation and representation, political behaviour, and political psychology.
@NenaOana
Carsten Q. Schneider is Professor of Political Science at Central European University Budapest.
His research focuses on regime transitions, autocratic regimes, the qualities of democracies, and the link between social and political inequalities. He also works in the field of comparative methodology, especially on set-theoretic methods.
Carsten has published in leading political science journals, and he is the author three books, among them Set-Theoretic Methods for the Social Sciences (Cambridge University Press, 2012).
The book Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction, co-authored with Ioana-Elena Oana and Eva Thomann, appeared in 2021 with Cambridge University Press and his book Set-Theoretic Multi-Method Research: A Guide to Combining QCA and Case Studies is forthcoming with the same publisher.
@CarstenQSchneidThe central aim of the course is to familiarise you with the formal logic of set-theoretic methods. It also aims to introduce QCA as an approach, its main assumptions, the technical environment (software) and the standard procedures and operations.
We will put particular emphasis on giving you a thorough understanding of the notions of necessity and sufficiency, because these are the nuts and bolts of QCA that set it apart from the majority of other cross-case comparative techniques.
Introduction
We introduce the course topic, the content and sequence of the course sessions, and the course resources. We also touch on the basics of set-theoretic methods, the epistemology of QCA, its variants, and look at how it compares with other standard qualitative and quantitative social scientific research designs. The centrepiece of this session is a demonstration of QCA on the basis of a recently published study.
Calibration and set theory
We address how to prepare observational data to perform QCA, i.e. how to calibrate. In so doing, we cover various modes of calibrating raw data for crisp-set, multi-value and fuzzy-set QCA.
We go through various calibration techniques using R, and discuss the consequences of different calibration decisions. We then turn to the methodological foundations of QCA, including a thorough discussion of the basic mathematical concepts of QCA, derived from set theory.
Set relations, causal complexity, and parameters of fit
We start by introducing the central notions of necessity and sufficiency, and discussing the so-called parameters of fit central to any QCA study, i.e. the measures of consistency, coverage, relevance of necessity, PRI.
We explore notions of causal complexity with a focus on INUS and SUIN causes. We then turn to ways of visualising patterns of necessity, SUIN conditions, and some methodological issues related to the parameters of fit.
Truth tables and logical minimisation
We turn to the analysis of sufficiency. We define the notion of a truth table in crisp-set and fuzzy-set QCA and consider how it differs from a data matrix.
We show how to analyse truth tables with respect to sufficient conditions to derive solution formulas. This includes the Quine-McCluskey Algorithm for the logical minimisation of sufficiency statements in a truth table.
Limited diversity and the standard analysis
We discuss the second problem of incomplete truth tables: logical remainder rows. We will explain how this phenomenon of limited diversity arises and which basic strategies are at the researcher’s disposal to mitigate its impact on drawing inferences.
Above all, we will show how counterfactual thinking can resolve problems of limited diversity. This leads to the development of 'intermediate solutions' in a so-called standard analysis.
We will analyse fake and real data using the R software environment and its QCA package. Prepared datasets will be available, but we also encourage you to bring your own raw data (even if still tentative), to use for exercises and project work.
Instructors and teaching assistants will be available for individual meetings to discuss your research project, answer questions regarding the design of a QCA study, explain how to 'format' raw data to render it compatible with QCA, and to discuss similar issues.
You will be able to navigate the course using pre-course material prepared by the Instructors, and via live lecture, discussion, and application sessions.
You are expected to follow the pre-course material before each live session. This consists of:
We expect you to follow this material before each online session.
Each online session will be around 1 hour 40 minutes per day. We will discuss pre-lecture material and joint practice of performing QCA in R, using adequate tools, such as RStudio Cloud.
In addition to the class discussion and joint programming exercises, the class will also have an online chat group (e.g. on Slack) where you can discuss course-related material and hold Q&A sessions with the Instructors and fellow participants.
You don’t need any prior knowledge of QCA or the R software environment and the package relevant for set-theoretic methods. However, you would profit from prior empirical-comparative training.
We strongly encourage you to familiarise yourself in advance with the basic principles of the QCA method, by reading the recommended literature.
A previous introduction to the basic functions of R and RStudio would be useful to start working with the software from day one. We will give you some 'Intro to R' material specific to QCA and we strongly encourage you to practice some of the basics (e.g. loading and manipulating a dataset) beforehand.
Prior knowledge of the very basics of formal logic and set theory would be very useful but is not expected.