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Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the last calendar year, you qualify for £45 off your course fee.
Monday 15 ꟷ Friday 19 March 2021
2 hours of live teaching per day
This course is taking place twice in one day
10:00-12:30 and 14:30-17:00 CET
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 (the Instructor plus one highly qualified Teaching Assistant) can cater to the specific needs of each individual.
This course introduces you to Qualitative Comparative Analysis and fuzzy sets, and their application in the social sciences, using the R software environment.
It starts out by familiarising you with the basic concepts of the underlying methodological perspective, including formal logic, Boolean algebra, causal complexity, and calibration. From there, we move to the central notions of necessity and sufficiency, and discuss ways to analyse these using parameters of fit and visualisation techniques.
The core of the course focuses on the logic and analysis of truth tables and discusses 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.
The use of QCA will be practiced based on data 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.
@CarstenQSchneidCourse introduction, set theory, and calibration
We introduce the course topic, the content and sequence of the sessions, and the course resources. We touch upon the basics of set-theoretic methods, the epistemology of QCA, its variants, and how it compares to other standard qualitative and quantitative social scientific research designs.
We turn to the methodological foundations of QCA, including a thorough discussion of the basic mathematical concepts of QCA, derived from set theory. We then address how to prepare observational data to perform QCA, i.e. how to calibrate. In doing so, we cover various modes of calibrating raw data for crisp-set, multi-value and fuzzy-set QCA. We will go through various calibration techniques using R and discuss the consequences of different calibration decisions.
Causal complexity, set relations and parameters of fit
We explore notions of causal complexity with a focus on INUS and SUIN causes. We introduce the central notions of necessity and sufficiency, and discuss the so-called parameters of fit central to any QCA study, i.e. the measures of consistency, coverage, relevance of necessity, PRI. We discuss ways of visualising patterns of necessity and sufficiency.
Analysis of sufficiency: the truth table analysis
This session is dedicated to the analysis of sufficiency using truth tables. We will 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 in order to derive solution formulas. This includes the Quine-McCluskey Algorithm for the logical minimisation of the sufficiency statements in a truth table.
Limited diversity
We take further the discussion on the truth table analysis and engage with the problem of incomplete truth tables: logical remainder rows. We 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 be used to resolve problems of limited diversity.
Standards of good QCA practice
We start by briefly reviewing what we learned throughout the course, above all with regard to the basics of the analysis of necessity and sufficiency.
Putting everything together, this session will focus on the truth table algorithm, i.e. the process from turning the data matrix into a truth table, then logically minimising the table, allowing for different strategies vis-à-vis the logical remainders, and calculating the parameters of fit for each solution formula.
We will ask you to apply the knowledge gained during the course to different published data sets and/or your own data, taking into account the standards of good QCA practice introduced throughout the course.
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:
Live sessions will each be around 100 mins per day, dedicated to discussions of pre-lecture material and joint practice of performing QCA in R, using adequate tools, such as Google Colab, RStudio Cloud, and/or Codeshare.
In addition to the class discussion and joint programming exercises, there will also be an online chat group (e.g. on Slack) where you can discuss course-related material and take part in 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 (such as the Comparative Research Designs course in Week 1) and we strongly encourage advance familiarisation 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 1. 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 are not expected.