C02-01AM: Advanced Applied Qualitative Comparative Analysis (QCA)

Qualitative Comparative Analysis continues to undergo modifications and improvements. This course enables students to produce a publishable QCA, applying the most advanced analytic tools available in the R software environment. The course builds on the content from the first week introductory QCA course.

The central aim of the course is to enable participants to put in practice state-of-the-art QCA empirical research. Apart from learning how to apply QCA in the best possible way, discussions in class will address from a set-theoretic point of view general methodological issues, such as robustness tests, theory evaluation, case selection strategies, or the role of time and temporality in descriptive and causal inference.

Day 1

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

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 Test Protocol.

Day 3

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

We introduce Set-Theoretic Multi-Method Research (SMMR) as 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

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.

Throughout the course, we will analyze 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, participants are encouraged to bring their own raw data (even if this data is still tentative), which can be used for lab exercises and project work. Instructors and teaching assistants will be available for individual appointments with course participants to discuss research projects, questions regarding the design of a QCA study, and similar issues. 

After this course you will be able to:

1. Implementing own QCA research based on current best practices and standards
2. Performing QCA in R, the most flexible and powerful software environment for this method
3. Distinguishing better from worse applications of QCA in published research
4. Getting familiar with R programming language

Recommended readings:

Schneider, Carsten Q., and Claudius Wagemann. 2012. Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge: Cambridge University Press.
Oana, Ioana-Elena, Carsten Q. Schneider, and Eva Thomann. 2021. Qualitative Comparative Analysis Using R: A Beginner’s Guide. Cambridge, MA: Cambridge University Press.   

Prerequisites

Please indicate what you expect from your participants in terms of prior study, skills, knowledge, experience, etc. This text should be rather specific. This text will be used on the RSS website and other marketing communication.

Participants should have some prior experience with QCA, roughly at the level of the material covered in the course ‘Introduction to QCA’. Prior (minimum) experience with R is not required, but it makes it easier to follow the course. In general, it is helpful if researchers have experience with comparative empirical social research and, ideally, they have their own research project in which they would like to use QCA.

This description is subject to change at the discretion of the Instructor

2 Credits

For completion of all work before and during the course, as outlined by the Instructor, and 90% participation and attendance of the course.

2 Additional Credits

Course specific extra assignments. These can include submitting assignments before the course, daily assignments, and/or a final assignment to be completed after the course as decided by the Instructor.  This description is subject to change at the discretion of the Instructor.