C01-01AM: Introduction to Qualitative Comparative Analysis (QCA)

This 5-day course introduces Qualitative Comparative Analysis as an approach and a technique, its main assumptions, its standard procedures and operations, and the technical environment (R software and packages) used for its application. QCA enables researchers to model causal complexity by analyzing whether different configurations of conditions are necessary or sufficient for an outcome, based on a formalized comparison of intermediate to large numbers of cases. Throughout the course, emphasis is put on a thorough understanding of the formal logic of set-theoretic methods and QCA, including topics such as Boolean algebra, causal complexity, sets and their calibration, necessity, and sufficiency. The course also discusses the logic and analysis of truth tables and the most important problems that emerge when this analytical tool is used for exploring social science data. Right from the beginning, participants are exposed to performing set-theoretic analyses with the relevant R software packages using data from published applications in the social sciences.

Day 1 – Introduction, Calibration and Set Theory

In Session 1, participants will be introduced to the course topic, the content and sequence of the course sessions, as well as the course resources. We will also touch upon the basics of set-theoretic methods, the epistemology of QCA, its different variants, and how it compares to other standard qualitative and quantitative social scientific research designs. We then address the question of how to prepare observational data to perform QCA, i.e. how to calibrate. In doing so, we will 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. We then turn to the methodological foundations of QCA including a thorough discussion of the basic mathematical concepts of QCA, which are derived from set theory.

Day 2 – Set Relations, Causal Complexity, and Parameters of Fit

This session will start by introducing the central notions of necessity and sufficiency and discussing the so-called parameters of fit that are central to any QCA study, i.e. the measures of consistency, coverage, relevance of necessity, PRI. We further explore notions of causal complexity with a focus on INUS and SUIN causes. We then turn to ways of visualizing patterns of necessity, SUIN conditions, and some methodological issues that are related to the parameters of fit.

Day 3 – Truth Tables and Logical Minimization

In session 3, we turn to the analysis of sufficiency. We will de ne the notion of a truth table in crisp-set and fuzzy-set QCA and how it differs from a data matrix. We will 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 minimization of the sufficiency statements in a truth table.

Day 4 – Limited Diversity and the Standard Analysis

In this session we will discuss the second problem of incomplete truth tables: logical remainder rows. We will 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. This leads to the development of intermediate solutions in a so-called Standard Analysis.

Day 5 – Limited Diversity and the Enhanced Standard Analysis

In the previous session, we learned the Standard Analysis procedure for handling logical remainders in QCA. In this session, we discuss and implement the Enhanced Standard Analysis (ESA). In a nutshell, it consists in excluding all untenable assumptions from the logical minimization. In addition, we `simply’ put together what we have learned up until this point and thus replicate the so-called Truth Table Algorithm, the modal algorithm for performing QCA.

Learning goals:

• Gain a thorough understanding of the general analytic goals and motivations underlying the use of QCA.
• Gain a thorough understanding of the formal logic underlying QCA, as well as of the notions of sets, causal complexity, necessity, and sufficiency.
• Be able to perform the main analytic steps involved in doing a QCA (calibration, analysis of necessity, analysis of sufficiency) using the relevant R software packages and social science data.
• Be able to identify potential pitfalls and problems that might emerge in applied QCA together with ways of avoiding them.
• Be able to interpret and visualize QCA results.

Mandatory Readings:

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

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.