When it comes to the discussion of the importance of QCA results, very different notions seem to exist. Authors make statements based on multiple references, such as how consistent their solution terms are (high consistencies are better than lower ones), how much of the outcome is explained/covered (again: the more the better), how many cases are described by the solution, or if those (few) cases which are covered are at least described very well.
In the literature, the only clear reference up to now how to define important (configurations of) conditions in QCA is the understanding of importance put forward by Goertz (and Mahoney) which focuses on conditions which are close to being at the same time necessary and sufficient for an outcome. However, this fits oddly with the general reasoning in QCA because it relates to a linear and symmetric idea of causality, partly masks the configurative foundations, and neglects the case-oriented roots of QCA.
We, therefore, propose a first systematic treatment of the question what the concept of importance means in a QCA design offering a new perspective on how to handle theoretical and empirical dimensions of importance in QCA using both illustrative and real-world data.
