Using fuzzy-set qualitative comparative analysis

Fuzzy-set qualitative comparative analysis (Fs/QCA) is a social science me thod developed in order to combine case-oriented and variable-oriented quantitative analysis. It started with the creation of qualitative comparative analysis [1], with Fs/QCA later developed by applying fuzzy-set theory [2]. Although the fuzzy-set approach is still considered to be in the process of being developed [3-5], it has already been discussed as a useful methodology among social scientists. In addition, the number of researchers across disciplines who use this methodology has steadily increased. Fs/QCA recognizes the limitation of case-oriented research in theorization and scientific measurement. In most cases, researchers begin their case-oriented research based upon theoretical concepts and then select cases to refine and elaborate on these concepts. In Fs/QCA, a set membership is scientifically calibrated by using empirical evidence. At the same time, a set-theoretic approach is incorporated to increase the connectivity between theories and methodologies, a limitation of variable-oriented research. In other words, case-oriented research has acquired some of the characteristics of variable-oriented research through calibration as well as retaining its own characteristics with the use of set theory. The first main characteristic of Fs/QCA is the degrees of freedom allowed for researchers to examine the causal complexities involved in conjunctural causation [2]. The fundamental aim of case-oriented comparative analysis is to examine cases configurationally. Because separate parts consisting of the whole are considered to be understood in the whole picture or configurationally in case-oriented analysis [2], researchers are concerned with understanding the context of a case. In addition, with the use of various theories, they look into various aspects of the case to obtain a comprehensive understanding of the homogeneity within it. Similarly, Fs/QCA focuses on a joint causal system that allows interaction effects among each characteristic within a case rather than a methodology in which the independent variables are considered constant and the effects of a cause on the dependent variables are examined. For example, in Fs/QCA, the independent effects of the health care system and democracy are not verified as a causal condition of “citizens’ health level.” On the contrary, four causal conditions are constructed as a configuration and the relationship between configurational conditions and health levels are analyzed. In other words, when the existence of the health care system (here, divided into low and high levels of health care systems) and democracy (here, divided into highly developed and underdeveloped democracies) are chosen as the hypothetical conditions accountable for citizens’ health level, each independent variable’s effect on citizens’ health level (the dependent variable) is not analyzed, rather how each condition is combined and how it functions as a factor are assessed. In Fs/QCA, causal conditions are explained as “attributes.” When there are K attributes, up to 2 causal combinations can be constructed mathematically (H means a high level of health care system, h a low level of health care system, D a highly developed democracy, and d an underdeveloped democracy). X1= high level of health care system with a highly developed democracy (HD), X2= high level of health care system with an underdeveloped democracy (Hd), X3= low level of health care system with a highly developed democracy (hD), X4= low level of health care system with an underdeveloped democracy (hd).