Advanced Qualitative Comparative Analysis

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.

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.