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Advanced Qualitative Comparative Analysis

Ioana-Elena Oana
nena.oana@yahoo.com

European University Institute

Ioana-Elena (Nena) 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
schneiderc@ceu.edu

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 articles in several peer-reviewed journals, and three books, among them Set-Theoretic Methods for the Social Sciences (Cambridge University Press, 2012) co-authored with Claudius Wagemann.

  @CarstenQSchneid
 

Course Dates and Times

Monday 22 – Friday 26 March 2021
2 hours of live teaching per day
14:30–17:00

Prerequisite Knowledge

You should have a firm command of basic formal logic, Boolean algebra, and set-theory and 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 minimization 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 ECPR Virtual Methods School in summer 2020, or the two-week course on QCA at the 2019 ECPR Summer School, you are well prepared for this advanced course.


Short Outline

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.

Purpose of the course

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 that are more compelling for you and for your (critical) audience.

We will try and 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 central aims of the course are fourfold:

  1. to revisit the core points of QCA addressed in week 1 (calibration, tests of necessity and sufficiency, truth tables, parameters of fit)
  2. to 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. to get better acquainted with the standards of good practice, both in its fundamental aspects and in using the relevant software programmes
  4. to 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 see 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 clusters, 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. It intersects theoretical expectations with empirical results generated with QCA. The findings from this procedure can be used to identify areas in which theory find 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 in an attempt at specifying 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. On this day, you will apply the knowledge gained during the course to different published data sets and/or your own data.


Throughout the course, we will analyse fake and real data in the computer lab, using the R software environment and packages QCA and SetMethods. In addition to prepared datasets, which will be made available, you are encouraged to bring your own raw data (even if this data is still tentative), for lab exercises and project work.

Instructors and teaching assistants will be available for individual appointments to discuss research projects, questions regarding the design of a QCA study, and similar issues.

How the course will work online

You will navigate the course using offline material prepared in advance by the Instructors, consisting of:

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

You are expected to follow this material before each online session.

The online sessions will each be around 100 mins per day and are dedicated to discussions of each day's pre-lecture material, and joint to practice of performing QCA in R (using adequate tools, such as RStudio Cloud).

We will also offer online chat groups (e.g. on Slack) where you can discuss course-related material, and take part in Q&A sessions with the Instructors and fellow participants.

 


Additional Information

Disclaimer

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 in due time.

Note from the Academic Convenors

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, contact the instructor before registering.