Using case‐based approaches to analyse large datasets: a comparison of Ragin’s fsQCA and fuzzy cluster analysis

The paper undertakes a comparison of Ragin's fuzzy set Qualitative Comparative Analysis with cluster analysis. After describing key features of both methods, it uses a simple invented example to illustrate an important algorithmic difference in the way in which these methods classify cases. It then examines the consequences of this difference via analyses of data previously calibrated as fuzzy sets. The data, taken from the National Child Development Study, concern educational achievement, social class, ability and gender. The classifications produced by fsQCA and fuzzy cluster analysis (FCA) are compared and the reasons for the observed differences between them are discussed. The predictive power of both methods is also compared, employing both correlational and set theoretic comparisons, using highest qualification achieved as the outcome. In the main, using the real data, the two methods are found to produce similar results. A final discussion considers the generalisability or otherwise of this finding.