Fuzzy set/Qualitative Comparative Analysis (fsQCA) (Ragin, 2000), is a method for identifying the configurations of conditions that lead to specific outcomes by providing evidence of causality in complex systems.
Qualitative Comparative Analysis (QCA), is used for causal complexity analysis (Longest & Vaisey, 2008). QCA underscores that causality is complex, characterised by three principles: 1) conjunction, referring to the notion that multiple, independent causal attributes jointly produce an outcome; 2) equifinality, suggesting that different combinations of conditions yield the same outcome; and 3) asymmetry, pertaining to the possibility that both presence and absence of attributes would be associated with the outcome.
How it works?
The set-theoretic approach of fsQCA uses Boolean algebra to determine which causal relationships (or combinations of antecedents) contribute to the outcome in question (Boswell and Brown, 1999, Ragin, 1987, Ragin, 2009). FsQCA proposes various alternative causal relationships to understand the construct of an outcome (Kraus et al., 2018).
The configurational and set-theoretic approach of QCA is an alternative to the logic of the variable as causal agent (Byrne, 1998; Ragin, 2008). In other words, there is a fundamental distinction between QCA and conventional variable-based approaches. QCA works with a particular conception of causality based on multiple conjunctural causation, by which is meant outcomes emerging from distinctive combinations of causes that are revealed in types of case and not independent variable effects (Berg-Schlosser et al., 2009). This is a non-linear, non-additive and non-probabilistic conception that stresses diversity, complex combinations of conditions and equifinality: different paths can lead to the same outcome.
Case Study: What kinds of countries have better innovation performance?–A country-level fsQCA and NCA study