Five-day QCA course (taught in Italian) with focus on the calibration of qualitative data. The course is part the School in Qualitative and Mixed Methods hosted by the Fondazione Marco Biagi and the University of Modena and Reggio Emilia (UniMORE), Italy.

Course Overview:

The course offers the theoretical and methodological foundations to perform QCA, from set-conceptualisation, to configurational thinking, to casing and data calibration. By means of examples and exercises, the course focusses on the collection and calibration of qualitative data, although the acquired knowledge and skills can be applied to any type of data for QCA performance. The participants will also take part in tutored classes in the afternoon to apply what they learned about QCA to their prospective or on-going research projects. The course also includes sessions on the practical application of QCA by using the R packages ‘QCA’ and ‘SetMethods’ in the R Studio environment by replicating selected published QCA papers.
More information on the course is available at this page.

Who should attend:

This course targets Italian-speaking master and doctoral students, postdoctoral researchers, and faculty across the social and human sciences who are interested in configurational thinking using set-analytic methods to examine causal complexity.

About QCA

Qualitative Comparative Analysis (QCA) has been introduced by Charles C. Ragin’s (1987) book ‘The Comparative Method.’ Rooted in historical comparative research in political sciences, QCA expands the logic of qualitative analysis to multiple case studies. By following a formalised workflow, QCA allows researchers to perform systematic cross-case comparison and assess the ‘fit’ of the obtained results in comparative terms by relying on specific parameters.

Most interesting to QCA is the ‘shift of mindset’ required along all the stages of the research, as cases have to be considered elements belonging to conditions (factors) and outcomes (‘effects’) conceptualised as sets. QCA rests on configurational thinking for the representation and explanation of the selected cases in the sample through the concepts of necessity and sufficiency, equifinality, multifinality and limited diversity, which are the main elements composing causal complexity which characterises QCA.

QCA is typically applied to samples composed of small- or intermediate-n (usually, 10-50 cases), but it is increasingly used by researchers to examine large-n datasets from a configurational and set-theoretic logic.

Contact Information

For questions or additional information, please contact dr. Sofia Pagliarin by writing to pagliarin@ihs.nl