Qualitative Comparative Analysis (QCA) und Fuzzy Sets

2007 ◽  
Author(s):  
Carsten Q. Schneider ◽  
Claudius Wagemann
Keyword(s):  
Comparative Analysis ◽  
Fuzzy Sets ◽  
Qualitative Comparative Analysis

Qualitative Comparative Analysis (QCA) and Set Theory

2017 ◽  
Author(s):  
Claudius Wagemann
Keyword(s):  
Comparative Analysis ◽  
Fuzzy Sets ◽  
Political Science ◽  
Set Theory ◽  
Social Scientist ◽  
A Priori ◽  
Qualitative Comparative Analysis ◽  
General Question ◽  
Explanatory Factors ◽  
The Stability

Qualitative Comparative Analysis (QCA) is a method, developed by the American social scientist Charles C. Ragin since the 1980s, which has had since then great and ever-increasing success in research applications in various political science subdisciplines and teaching programs. It counts as a broadly recognized addition to the methodological spectrum of political science. QCA is based on set theory. Set theory models “if … then” hypotheses in a way that they can be interpreted as sufficient or necessary conditions. QCA differentiates between crisp sets in which cases can only be full members or not, while fuzzy sets allow for degrees of membership. With fuzzy sets it is, for example, possible to distinguish highly developed democracies from less developed democracies that, nevertheless, are rather democracies than not. This means that fuzzy sets account for differences in degree without giving up the differences in kind. In the end, QCA produces configurational statements that acknowledge that conditions usually appear in conjunction and that there can be more than one conjunction that implies an outcome (equifinality). There is a strong emphasis on a case-oriented perspective. QCA is usually (but not exclusively) applied in y-centered research designs. A standardized algorithm has been developed and implemented in various software packages that takes into account the complexity of the social world surrounding us, also acknowledging the fact that not every theoretically possible variation of explanatory factors also exists empirically. Parameters of fit, such as consistency and coverage, help to evaluate how well the chosen explanatory factors account for the outcome to be explained. There is also a range of graphical tools that help to illustrate the results of a QCA. Set theory goes well beyond an application in QCA, but QCA is certainly its most prominent variant. There is a very lively QCA community that currently deals with the following aspects: the establishment of a code of standards for QCA applications; QCA as part of mixed-methods designs, such as combinations of QCA and statistical analyses, or a sequence of QCA and (comparative) case studies (via, e.g., process tracing); the inclusion of time aspects into QCA; Coincidence Analysis (CNA, where an a priori decision on which is the explanatory factor and which the condition is not taken) as an alternative to the use of the Quine-McCluskey algorithm; the stability of results; the software development; and the more general question whether QCA development activities should rather target research design or technical issues. From this, a methodological agenda can be derived that asks for the relationship between QCA and quantitative techniques, case study methods, and interpretive methods, but also for increased efforts in reaching a shared understanding of the mission of QCA.

scholarly journals Standards of Good Practice in Qualitative Comparative Analysis (QCA) and Fuzzy-Sets

2010 ◽  
Vol 9 (3) ◽  
pp. 397-418 ◽  
Author(s):  
Carsten Q. Schneider ◽  
Claudius Wagemann
Keyword(s):  
Data Analysis ◽  
Comparative Analysis ◽  
Fuzzy Sets ◽  
Good Practice ◽  
Qualitative Comparative Analysis ◽  
Research Approach ◽  
Analytical Technique ◽  
Work In Progress ◽  
Methodological Tool

AbstractAs a relatively new methodological tool, QCA is still a work in progress. Standards of good practice are needed in order to enhance the quality of its applications. We present a list from A to Z of twenty-six proposals regarding what a “good” QCA-based research entails, both with regard to QCA as a research approach and as an analytical technique. Our suggestions are subdivided into three categories: criteria referring to the research stages before, during, and after the analytical moment of data analysis. This listing can be read as a guideline for authors, reviewers, and readers of QCA.

Qualitative comparative analysis: Crisp and fuzzy sets in business and management

2016 ◽  
Vol 69 (4) ◽  
pp. 1261-1264 ◽  
Author(s):  
Norat Roig-Tierno ◽  
Kun-Huang Huarng ◽  
Domingo Ribeiro-Soriano
Keyword(s):  
Comparative Analysis ◽  
Fuzzy Sets ◽  
Qualitative Comparative Analysis ◽  
Business And Management

scholarly journals Condições à cooperação brasileira na África (2003–2010)

2019 ◽  
Vol 14 (3) ◽  
Author(s):  
David Beltrão Simons Tavares Albuquerque ◽  
Eduardo Matos Oliveira
Keyword(s):  
Comparative Analysis ◽  
Fuzzy Sets ◽  
Qualitative Comparative Analysis ◽  
Per Capita

A proposta desse trabalho é buscar os aspectos estruturais da inserção brasileira na África, por meio da análise das condições necessárias e suficientes para o estabelecimento de projetos de cooperação brasileira na África entre os anos de 2003 a 2010. As quatro condições observadas, por meio da análise teórica e empírica de recentes trabalhos, foram se um país tem como língua matriz o português, o grau de estabilidade política, o PIB per capita e o déficit alimentar. O artigo busca complementar as linhas teóricas tradicionais, as quais identificam os possíveis incentivos dos Estados subdesenvolvidos para promover a cooperação, a fim de indicar quantitativa e qualitativamente as políticas da Cooperação Sul-Sul (CSS) brasileira na África e seus respectivos impactos. Foi empregada, para tanto, a técnica de análise Qualitative Comparative Analysis fuzzy-sets (QCAfs). A variável dependente (qualitative outcome) abordará uma gradação de intervalo entre potenciais parceiros e não-parceiros, por meio da análise dos projetos e das condições existentes. Os resultados observados apontam como condição suficiente a presença da Língua Portuguesa.

scholarly journals The Configuration of Supply Chain Concentration and Staff Structure in Intelligent Manufacturing: A Fuzzy Sets Qualitative Comparative Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jie Ding ◽  
Siqi Wang ◽  
Meilan Chen
Keyword(s):  
Supply Chain ◽  
Comparative Analysis ◽  
Fuzzy Sets ◽  
Business Performance ◽  
Qualitative Comparative Analysis ◽  
Intelligent Manufacturing ◽  
Saturation State ◽  
Sales Personnel ◽  
Manufacturing Enterprises ◽  
Concentration Degree

In traditional manufacturing enterprises, there are common problems of low added value of products, low profit, and poor business performance. As a result, they endeavor to transform themselves into intelligent manufacturing. To help with their transformation, this paper proposes a decision support model for managers to improve the business performance under different configurations of supply chain concentration and staff structure. Through the fuzzy set qualitative comparative analysis, the membership degree is given to the variables, and then the configuration analysis is carried out. We find that, to facilitate intelligent manufacturing, the concentration degree of supply chain or the structure of employee education should be adjusted according to the results from the qualitative comparative analysis of fuzzy sets. Two configuration paths to improve business performance are found. When the supply chain concentration degree is relatively decentralized, manufacturing enterprises should expand the proportion of sales personnel and production personnel. In other words, when the sales personnel and production personnel reach the saturation state, low concentration of suppliers and customers is more conducive to the improvement of business performance. The configuration of high proportion of production personnel and low customer concentration tends to lock enterprises in the lower end of the value chain. Therefore, it is critical for enterprises to improve the education level of employees to transform into intelligent manufacturing and improve their business performance.

The Choice between Crisp and Fuzzy Sets in Qualitative Comparative Analysis and the Ambiguous Consequences for Finding Consistent Set Relations

Field Methods ◽  
2019 ◽  
Vol 32 (1) ◽  
pp. 75-88
Author(s):  
Ingo Rohlfing
Keyword(s):  
Comparative Analysis ◽  
Fuzzy Sets ◽  
Empirical Research ◽  
Qualitative Comparative Analysis ◽  
Consistency Score ◽  
Set Relations ◽  
The Relationship

Empirical researchers using qualitative comparative analysis (QCA) can work with crisp, multivalue, and fuzzy sets. The relative advantages of crisp and multivalue sets have been discussed in the QCA literature. There has been little reflection on the more frequent decision between crisp and fuzzy sets for which there often is no theoretical guidance. A review shows that researchers often prefer fuzzy over crisp sets, sometimes because they contain more information. This meets with the argument that fuzzy sets produce more conservative consistency measures and constitute tougher tests. In my article, I demonstrate analytically and with data from published QCA studies that the relationship between crisp sets, fuzzy sets, and the consistency score is ambiguous. It depends on the distribution of cases whether the consistency value is more or less conservative for fuzzy sets than for crisp sets. I outline the implications of the ambiguous relationship for empirical research.

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