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Advanced Qualitative Comparative Analysis

Ioana-Elena Oana

European University Institute

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. 



Carsten Q. Schneider

Central European University

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.


Course Dates and Times

Monday 14 – Friday 18 February 2022
2 hours of live teaching per day
15:30 – 17:15 CET

VIR: This is a virtual course

Prerequisite Knowledge

You should have a firm command of basic formal logic, Boolean algebra, and set theory. You should also be familiar with the basics of the R software environment and R packages relevant for performing set-theoretic analyses. In particular, you must be familiar with the basic protocol of Qualitative Comparative Analysis (QCA), including:

  • the difference between sets and variables
  • the notion of set calibration
  • the meaning of set relations (sufficiency, necessity, INUS, SUIN)
  • the construction and logical minimisation of a truth table
  • the calculation and interpretation of the parameters of fit (consistency and coverage)
  • the treatment of logical remainders as done by the standard analysis.

If you attended the one-week course on Qualitative Comparative Analysis (QCA) at the 2021 Virtual Winter School or the two-week course on QCA at the 2021 Virtual Summer School, you are well prepared for this advanced course.

Short Outline

If you have good knowledge of all the elements in the Prerequisite knowledge section above, this course will deepen your understanding of the potentials and pitfalls of set-theoretic methods.

The skills you gain will enable you to be more critical and assertive if and when you choose or reject set-theoretic methods as the most appropriate research method for your research project.

By the end of this course, you will be able to produce QCA studies of a quality and level of sophistication beyond the current mainstream and thus yield substantive results more compelling for you and your (critical) audience.

We will try to address all the following topics but, depending on participants' needs and interests, we can put more emphasis on some:

  • Set-theoretic multi-method research (SMMR)
  • Set-theoretic robustness and sensitivity
  • Set-theoretic theory evaluation
  • Enhanced Standard Analysis
  • Data structures and set-theoretic methods, including temporal ordering and two-step QCA
  • Model ambiguity
  • Multi-value QCA
ECTS Credits

3 credits Engage fully with class activities 
4 credits Complete a post-class assignment

Long Course Outline

The four central aims of this course are to

  1. Revisit core points of QCA addressed in the Week 1 course: calibration, tests of necessity and sufficiency, truth tables, parameters of fit
  2. Elaborate on further issues that arise when neat formal logical tools and concepts, such as necessity, sufficiency, and truth tables, are applied to social science data (mainly the issues of limited diversity and the challenge to make good counterfactuals on so-called logical remainders)
  3. Get better acquainted with the standards of good practice, in its fundamental aspects and in using the relevant software programmes
  4. Discuss general methodological issues such as robustness and theory evaluation from a set-theoretic point of view. 
Day 1

Enhanced Standard Analysis
We address the issue of limited diversity and introduce several amendments to the standard analysis. In addition to distinguishing between easy and difficult counterfactuals, we introduce the notion of tenable and untenable assumptions on remainders, and introduce the Enhanced Standard Analysis.

Day 2

Robustness test and sensitivity diagnostics
We introduce various perspectives on the ‘robustness’ or ‘sensitivity’ of results obtained with QCA. We discuss against which analytic decisions a result ought to be robust, and how. We also look at if and when a result can be considered robust (enough). We condense all this into a QCA robustness check protocol.

Day 3

Cluster diagnostics and theory evaluation
We first discuss strategies for confronting situations when the data at hand contains clusters that are potentially analytically relevant but have not been captured during the truth table analysis. These clusters can be of any kind, such as temporal, geographic, or substantive, and we explain how to probe whether the result obtained for the pooled (i.e. across clusters) data holds for all clustered separately.

We then continue with explaining and applying set-theoretic theory evaluation, which intersects theoretical expectations with empirical results generated with QCA. The findings from this procedure can be used to identify areas in which theory finds empirical support and where it does not.

Theory evaluation can also be used to identify most-likely and least-likely cases that are or are not confirmed by our QCA, information that can be used for selecting cases for further empirical scrutiny.

Day 4

Set-theoretic multi-method research (SMMR)
We introduce SMMR to try and specify just how QCA should be combined with within-case process tracing. We define the meaning of typical and deviant cases after a QCA, spell out the different rationales for studying each of them, and provide formulas for selecting the best available cases for (comparative) within-case analysis after a QCA. 

Day 5

Integrating time in QCA and standards of good practice
We discuss various analytic strategies for integrating the temporal dimension into QCA. We show how this can be done via calibration, causal chains/Coincidence Analysis (cna), an updated version of the two-step QCA approach, and temporal QCA (tQCA).

Finally, we put together the material of the entire course by spelling out standards of good practice highlighted throughout the course. During this day, we will ask you to apply knowledge gained during the course to different published datasets and/or to your own data.

Throughout the course

We will analyse fake and real data, using the R software environment, and packages QCA and SetMethods. We will make prepared datasets available, but we also encourage you to bring your own raw data (even if still tentative), to use for lab 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, and to discuss similar issues.

How the course will work online

You will navigate the course using offline material prepared by the Instructors, and via online lecture, discussion, and application sessions. Material provided for each session will consist of:

  1. targeted class readings
  2. pre-recorded lectures using slides and a whiteboard
  3. online test questions and R exercises

We expect you to work through these materials before each online session.

Online sessions will each 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.

Additional Information


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.