Day 1

• 6:30-7:00, Introductory session:
  • Who is who? Why have you chosen this course? What do you expect to get out of it?
  • What are we going to do over the next two days? What are we not going to do?
• 7:00-8:00, Epistemological foundations of QCA:
  • What are the epistemological foundations of QCA?
  • Where is QCA positioned in relation to case-oriented and variable-oriented research?
• 8:00-9:30, Exercise
  • The essence of QCA: logical minimization (and truth tables)
  • To get an understanding of the very basics of QCA, we will carry out a QCA analysis by hand.
  • Once we get the basics of QCA, we will explore data with an Excel add-in.

Day 2

• 6:30-7:00, Conjunctural causation, equifinality, (data) asymmetry
  • What is conjunctural causation? What is equifinality? What is (data) asymmetry? Where do we see it in the world?
  • Whether/where do you expect conjunctural causation, equifinality, and/or (data) asymmetry in your field of research?
• 7:00-8:00, What can QCA be used for?
  • Data analysis, literature reviews, theory building, case selection, robustness tests
• 8:00-9:00, Further and future development of QCA as a method
  • Crisp set (cs) and fuzzy set (fs) QCA
  • Ongoing critique to QCA as a method
• 9:00-9:30, Open discussion, recap of the basic terminology and concepts:
  • Condition, outcome, conjunctural causation, equifinality, asymmetry, necessary condition, sufficient condition, logical minimisation.

Day 3

• 6:30-8:00, What ahve we learned so far?
  • Exploring more datasets using the Excel add-in.
• 8:00-8:30, How easy is it to use QCA?
  • Jeroen’s experiences with using QCA in small and multi-year research projects.
• 8:30-9:30, The most common challenges when applying QCA (and how to deal with them)
  • Is the number of cases you have all that matters?
  • How many conditions can be included in the theoretical model?
  • What is limited diversity and what to do about it?

Day 4

• 6:30-7.30, Step 4: Calibration of Data
  • What is data calibration? Why is it so important (and often time consuming) in QCA?
  • How to Calibrate your data (for csQCA and fsQCA).
• 7:30-8:00, Open discussion, calibrating your own data
  • What challenges do you expect to encounter in callibrating your own data?
• 8:30-9:30, Important research design choices
  • Good practice to follow (a guideline)
  • Open discussion: where, why and how do you want to use QCA in your (future) research?
  • What are your hopes and concerns in applying QCA?