ECPR Winter School: Comparative Research Design
2021-03-15 - 2021-03-19 | Online
This course provides training on methods that enable a researcher to construct a solid and well-argued comparative research design (CRD) – any research enterprise that comprises at least two ‘cases’ or units of analysis.
We will cover multiple options, from very few cases (small-n) to multiple cases (intermediate-n) to many cases (larger-n), and in particular options that are QCA-compatible.
By the end of this course, you will be able to write up a well-constructed CRD section for your project and to reflect strategically on your comparative research project.
Key topics covered
We will cover a logical sequence of topics that constitute the core building blocks of a solid CRD:

Thinking upstream: why go comparative? What is the added value of comparison? What should be the mindset of a good comparative researcher? What is the link between a research puzzle and the choice for a CRD? How to formulate a comparative research question?
‘Casing’ operations: what are my cases, how to define them, conceptually and empirically? At which level(s) (micro, meso, macro) can they be apprehended? And what about the time dimension?
Which case selection strategy to choose? How many cases and which ones? Should I go ‘smaller-n’ or ‘larger-n’? Should I select cases with similar or different outcomes? Which basic case selection strategies are available, and what are the pros and cons? And what about more advanced strategies, e.g. ‘nested’ (multilevel) designs, designs including multiple time periods, etc?
How to systematically collect good-quality data when covering multiple cases? What are the tricks of the trade? How to gain sufficient ‘intimacy’ with the respective cases (case-based-knowledge)? And how to compile and manage this data?
How to engage in comparative data analysis? Which toolbox(es) to select, among ‘qualitative’ (case-oriented), specifically comparative (in particular QCA – Qualitative Comparative Analysis), and ‘quantitative’ (statistical, variable-oriented) data analysis techniques?

We’ll unpack topics 1, 2 and 3 in detail, and take a bird’s eye view of topics 4 and 5 because they refer to multiple methods and techniques (including QCA).
This seminar-type 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 12 participants so that the Instructor can cater to the specific needs of each individual.
Prerequisite Knowledge
Little prior knowledge is expected. Any training in qualitative and/or quantitative methods would be an asset, but is by no means a requirement. You should simply be willing to reflect openly about your research design – there is no ‘best’ or ‘one-size-fits-all’ comparative research design.

ECPR Winter School: Qualitative Comparative Analysis
2021-03-15 14:30 - 2021-03-19 17:00 (Europe/London)
This course introduces you to Qualitative Comparative Analysis and fuzzy sets, and their application in the social sciences, using the R software environment.
It starts out by familiarising you with the basic concepts of the underlying methodological perspective, including formal logic, Boolean algebra, causal complexity, and calibration. From there, we move to the central notions of necessity and sufficiency, and discuss ways to analyse these using parameters of fit and visualisation techniques.
The core of the course focuses on the logic and analysis of truth tables and discusses the most important problems that emerge when this analytical tool is used for exploring social science data.
Right from the beginning, you will perform set-theoretic analyses with the relevant R software packages. When discussing set-theoretic methods, our debates will engage on broad, general comparative social research issues, such as case selection principles, concept formation, questions of data aggregation, and the treatment of causally relevant notions of time.
The use of QCA will be practiced based on data from published applications in the social sciences.
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.
Prerequisite Knowledge
You don’t need any prior knowledge of QCA or the R software environment and the package relevant for set-theoretic methods. However, you would profit from prior empirical-comparative training (such as the Comparative Research Designs course on that same week, A:M) and we strongly encourage advance familiarisation with the basic principles of the QCA method by reading the recommended literature.
A previous introduction to the basic functions of R and RStudio would be useful to start working with the software from day 1. We will give you some Intro to R material specific to QCA and we strongly encourage you to practice some of the basics (e.g. loading and manipulating a dataset) beforehand.
Prior knowledge of the very basics of formal logic and set theory would be very useful but are not expected.
