Fuzzy Set Qualitative Comparative Analysis (fsQCA)
Fuzzy-set qualitative comparative analysis (fsQCA) is a set-theoretic method that identifies which combinations of conditions are necessary or sufficient for an outcome, using cases calibrated to degrees of set membership.
퍼지셋 질적비교분석(fsQCA)은 사례를 집합 소속 정도로 보정한 뒤 어떤 조건 조합이 결과의 필요조건 또는 충분조건이 되는지를 찾는 집합이론적 방법입니다.
How do I find which combinations of conditions lead to an outcome across a set of cases?
여러 사례에서 어떤 조건들의 조합이 결과로 이어지는지 어떻게 찾나요?
Fuzzy-set qualitative comparative analysis calibrates each condition and the outcome into membership scores between 0 and 1, then uses Boolean minimization of a truth table to find configurations of conditions that are consistently sufficient for the outcome. It fits small- to medium-N comparative studies that expect equifinality, where several different combinations can produce the same outcome.
When to use Fuzzy Set Qualitative Comparative Analysis (fsQCA)
- Small to medium number of cases, such as organizations, regions, or countries
- Expectation that different combinations of conditions lead to the same outcome
- Interest in necessary and sufficient conditions rather than average net effects
When to use something else
- Estimating the average net effect of each variable: use regression analysis
- Conditions and outcome are naturally binary: use crisp-set QCA
Assumptions and requirements
- Theory-based calibration with anchors for full membership, crossover, and full non-membership
- Substantive knowledge of the cases
- Frequency and consistency thresholds set for the truth table (raw consistency commonly at least 0.80)
- Necessity analysis performed before sufficiency analysis
What the analysis reports
- Calibrated membership scores
- Necessity analysis with consistency and coverage
- Truth table
- Complex, parsimonious, and intermediate solutions
- Solution consistency and coverage
How to report it
The intermediate solution comprised [k] configurations, with an overall solution consistency of [value] and solution coverage of [value].
Related methods
- Crisp-Set QCA
- Multiple Linear Regression
- Configurational Analysis
Run Fuzzy Set Qualitative Comparative Analysis (fsQCA) on Quorant
- Data you need
- a table (CSV or Excel) with an outcome variable and predictor (feature) columns
- Price
- 150 credits for one analysis up to 10,000 rows (complexity level 1); larger or wider data costs more. Optional methodologist review: 900 credits.
- Checking
- Python results are cross-checked against R reference outputs in every deployment
- Before you pay
- A free pre-analysis diagnosis checks your data and gives an exact quote; nothing is deducted until you approve it.