Qualitative Comparative Analysis is a systematic method of studying data on multiple comparable cases from about N=8 through to large datasets of N=10,000 etc. The QCA methods firstly involve casing, i.e. delineating cases; secondly organising a systematic data matrix (we will show these in NVIVO and in Excel); thirdly examining sets of cases known as configurations; fourth interpreting these in terms of ‘necessary cause’ and ‘sufficient cause’ of each major outcome of interest.  We demonstrate the fsQCA software for QCA. A fuzzy set is a record of the membership score of a case in a characteristic or set.  A crisp set is a membership value of 0 (not in the set) or 1 (fully in the set), and thus is a simplified measure compared with a fuzzy set. Fuzzy sets or crisp sets, and combinations can be used in QCA.  All the permutations of the causal factors, known as X variates, are considered one by one.  We test whether X is necessary, or sufficient, or both, for an outcome Y.  We then augment the standard measures of ‘consistency’.  We show that one can generate both within-group and sample-wide consistency levels for testing sufficient cause.

This one-day training course will attract those doing case-study research, those doing comparative research, and those who want to extend their skills in fuzzy set analysis from beginner to intermediate levels. It will suit qualitative as well as quantitative and mixed-methods researchers; all are welcome.