Key topics covered
Course introduction, set theory, and calibration
We introduce the course topic, the content and sequence of the sessions, and the course resources. We touch upon the basics of set-theoretic methods, the epistemology of QCA, its variants, and how it compares to other standard qualitative and quantitative social scientific research designs.
We turn to the methodological foundations of QCA, including a thorough discussion of the basic mathematical concepts of QCA, derived from set theory. We then address how to prepare observational data to perform QCA, i.e. how to calibrate. In doing so, we 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.
Causal complexity, set relations and parameters of fit
We explore notions of causal complexity with a focus on INUS and SUIN causes. We introduce the central notions of necessity and sufficiency, and discuss the so-called parameters of fit central to any QCA study, i.e. the measures of consistency, coverage, relevance of necessity, PRI. We discuss ways of visualising patterns of necessity and sufficiency.
Analysis of sufficiency: the truth table analysis
This session is dedicated to the analysis of sufficiency using truth tables. We will define the notion of a truth table in crisp-set and fuzzy-set QCA, and consider how it differs from a data matrix. We 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 minimisation of the sufficiency statements in a truth table.
We take further the discussion on the truth table analysis and engage with the problem of incomplete truth tables: logical remainder rows. We 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.
Standards of good QCA practice
We start by briefly reviewing what we learned throughout the course, above all with regard to the basics of the analysis of necessity and sufficiency.
Putting everything together, this session will focus on the truth table algorithm, i.e. the process from turning the data matrix into a truth table, then logically minimising the table, allowing for different strategies vis-à-vis the logical remainders, and calculating the parameters of fit for each solution formula.
We will ask you to apply the knowledge gained during the course to different published data sets and/or your own data, taking into account the standards of good QCA practice introduced throughout the course.
How the course will work online
You will be able to navigate the course using pre-course material prepared by the Instructors, and via live lecture, discussion, and application sessions.
You are expected to follow the pre-course material before each live session. This consists of:
- targeted class readings
- around 40 minutes of pre-recorded lectures using slides and a whiteboard
- online test questions and R exercises.
Live sessions will each be around 100 mins per day, dedicated to discussions of pre-lecture material and joint practice of performing QCA in R, using adequate tools, such as Google Colab, RStudio Cloud, and/or Codeshare.
In addition to the class discussion and joint programming exercises, there will also be an online chat group (e.g. on Slack) where you can discuss course-related material and take part in Q&A sessions with the Instructors and fellow participants.