What is fuzzy set qualitative comparative analysis?

Fuzzy-set qualitative comparative analysis (Fs/QCA) is a social science method developed in order to combine case-oriented and variable-oriented quantitative analysis. Fs/QCA recognizes the limitation of case-oriented research in theorization and scientific measurement.

How do I download fsQCA?

Go to Visual Studios Downloads (scroll down) and click on ‘Other Tools and Frameworks. ‘ Next to ‘Microsoft Visual C++ Redistributable for Visual Studio 2017,’ click ‘x86’ and then ‘Download. ‘ After installing the redistributable files, fsQCA 3.1b for Windows should start normally.

What is Fscqa?

Fuzzy Set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It was developed by the social scientist Prof. Charles C. Ragin.

How do I run fsQCA?

In order to run the fsQCA algorithm choose “Analyze > Truth Table Algorithm”, and at this point the researcher has to select the variables that will be included in the analysis (Figure 4). In detail, the causal conditions are the independent variables, and the outcome is the dependent variable.

Is a comparative study qualitative?

Qualitative Comparative Analysis (QCA) is a methodology that enables the analysis of multiple cases in complex situations. It can help explain why change happens in some cases but not others. QCA is a case-based approach.

What is fuzzy set methodology?

Fuzzy set theory is a research approach that can deal with problems relating to ambiguous, subjective and imprecise judgments, and it can quantify the linguistic facet of available data and preferences for individual or group decision-making (Shan et al., 2015a).

Is comparative analysis qualitative or quantitative?

Qualitative comparative analysis approach draws strength from both quantitative and qualitative research methods. It combines the mathematical approaches used in quantitative research with the inductive and comparative case-based techniques employed in qualitative research.

What is crisp set QCA?

Abstract. Crisp-set qualitative comparative analysis (csQCA), a research approach developed by Charles Ragin in the 1980s, aims to combine qualitative and quantitative research strategies. Applications of the method have appeared in numerous journals.

What is multi value QCA?

Multi-value QCA, as the name suggests, is an extension of csQCA. It retains the main principles of csQCA, namely to perform a synthesis of a data set, with the result that cases with the same outcome value are “covered” by a parsimonious solution (the minimal formula).

What are the examples of comparative research?

Examples of ongoing comparative research surveys include the Gallup Polls (since 1945), the General Social Survey (since 1972), the Eurobaromètre (since 1973), the European Community Household Study (since 1994), and the International Social Survey Program (ISSP), which, since 1984, has conducted general social …

How to use fuzzy set qualitative comparative analysis?

Fuzzy Set Qualitative Comparative Analysis (fsQCA) 1 Outline. Qualitative Comparative Analysis is a systematic method of studying data on multiple comparable cases from about N=8 through to large datasets of N=10,000 etc. 2 Objectives. 3 Prerequisites. 4 Recommended reading. 5 About the instructor. 6 Apply

How are fuzzy sets and crisp sets used in QCA?

We demonstrate the fsQCA software for QCA. A fuzzy set is a record of the membership score of a case in a characteristic or set. A crisp set is a membership value of 0 (not in the set) or 1 (fully in the set), and thus is a simplified measure compared with a fuzzy set. Fuzzy sets or crisp sets, and combinations can be used in QCA.

How are fuzzy sets used in data analysis?

Fuzzy sets are especially powerful because they allow researchers to calibrate partial membership in sets using values in the interval between [0] (nonmembership) and [1] (full membership) without abandoning core set theoretic principles such as, for example, the subset relation.

How are fuzzy sets used in causal complexity analysis?

As Ragin (2000) demonstrates, the subset relation is central to the analysis of causal complexity. While fuzzy sets solve the problem of trying to force-fit cases into one of two categories (membership versus nonmembership in a set) or into one of three or four categories 1 (mvQCA), they are not well suited for conventional truth table analysis.