FSQCAFuzzy Set/Qualitative Comparative Analysis
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The third step is the application of the QCA method, which is an umbrella term for several subtypes, including fsQCA [46].
Calibrating is the first step of fsQCA. As the initial sample data failed to satisfy Boolean logic, we necessarily converted them into aggregate data falling in the interval of [0, 1].
Next, we introduce and explain the purpose and key steps of fsQCA. This is followed by the presentation of the results.
This was carried out with the fsQCA 2.5 software, which employs a logistic function to fit raw data between three qualitative breakpoints: full membership (1), the cross-over point (0.5) and full non-membership (0) (Ragin and Davey, 2014).
For this purpose, I will use the method of fuzzy set Qualitative Comparative Analysis (fsQCA), which is well suited for investigating relationships of necessity and sufficiency.
Probabilistic criteria can be applied to fsQCA results to evaluate the likelihood that an observed consistency is different from a benchmark value (Ragin 2000, pp.
However, the standard practice (following Schneider & Wagemann 2007, and Ragin & Rihoux 2009) is now to distinguish between three labels: (1) when referring to the original Boolean version of QCA, we use csQCA (where "cs" stands for "crisp set"); (2) when referring to the version that allows multiple-category conditions, we use mvQCA (where "mv" stands for "multi-value"); (3) when referring to the fuzzy set version which also links Fuzzy Sets to truth table analysis, we use fsQCA (where "fs" stands for "fuzzy set").
We use fuzzy set qualitative comparative analysis (fsQCA) to assess the set theoretic connections among configurations and employment modes in a sample of 74 owners of businesses in five industry groups in existence less than seven years.
Finally, we empirically test the validity of our structured typology of competitive priorities by employing a newly emerged technique known as fuzzy set qualitative comparative analysis (FsQCA) (Ragin, 2000; 2008), which enables researchers to model causal relationships in terms of set-theoretic relations (Fiss, 2011).