Exploring the Implications of Fuzzy-set Analysis Method in Social Science: Beyond the Border of Qualitative and Quantitative Research Methodologies
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This study explores the implications of Fuzzy-set Analysis method in social science, which combines a case-based qualitative method with a variable-driven quantitative method, and look at social diversity through comparative studies. The Fussy-set Analysis method as QCA (qualitative comparative analysis) can deal with existing case studies and intermediate cases that regression analysis can not address (15~50 cases). It consists of Fussy-set Multiple Conjunctural Analysis and Fussy-set Ideal Type Analysis. The former defines the relationship between cause and effect as a necessary condition or a sufficient condition, through the technical processes: ‘Calibration’, ‘Operation’, ‘Evaluating set relations’ and ‘Reduction’. The latter analyzes the degree of belonging to each ideal type and the direction of the time series change by converting to the fuzzy membership score of continuous value between 0 and 1. In short, the fuzzy set analysis will allow active penetration into the areas covered by the variable-centric approach. It can be an alternative to the blind spot that the existing methodologies could not deal with and at the same time it can overcome these weaknesses.
Abstract
This study explores the implications of Fuzzy-set Analysis method in social science, which combines a case-based qualitative method with a variable-driven quantitative method, and look at social diversity through comparative studies. The Fussy-set Analysis method as QCA (qualitative comparative analysis) can deal with existing case studies and intermediate cases that regression analysis can not address (15~50 cases). It consists of Fussy-set Multiple Conjunctural Analysis and Fussy-set Ideal Type Analysis. The former defines the relationship between cause and effect as a necessary condition or a sufficient condition, through the technical processes: ‘Calibration’, ‘Operation’, ‘Evaluating set relations’ and ‘Reduction’. The latter analyzes the degree of belonging to each ideal type and the direction of the time series change by converting to the fuzzy membership score of continuous value between 0 and 1. In short, the fuzzy set analysis will allow active penetration into the areas covered by the variable-centric approach. It can be an alternative to the blind spot that the existing methodologies could not deal with and at the same time it can overcome these weaknesses.
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