Fuzzy-set qualitative comparative analysis (fsQCA) is a relatively new method used in the social sciences to analyze whether a set of causal conditions (

) is necessary (

), sufficient (

), or both necessary and sufficient (

) for an outcome (

) to occur. These subset-theoretic relations are assessed on the basis of consistency (

) and coverage (

). The left graphic shows how four popular membership functions assign fuzzy condition set membership scores to 30 cases from a normally distributed base variable (

). This assignment is based on the location of the crossover anchor (

), which defines the point of maximum set membership ambiguity at 0.5. Functions include the linear function

, the quadratic function

, the root function

, and the logistic function

. The right graphic in the middle visualizes the resulting subset-theoretic relation for the case of sufficiency. The table on the right shows how

and

change as a result of altering

,

, or

. Different outcome set scores can be generated by changing the seed.