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Course syllabus:

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?

During the course, there will be the opportunity for participants to have a one-hour one-on-one meeting with the instructor outside class hours.

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