Calibration

FsQCA is a methodological tool that uses calibrated data to transform categorical, demographic and Likert scale variables into transformed conditions with values ranging from zero to one. The process of ranking conditions from full membership to non-membership is known as calibration. In order to transform the variables measured with the Likert scale (OLC, ITS and PRAC) into a fuzzy set, the mean values of the items must be calculated [70]. Our measurement scale was a seven-point scale, and so we identified total non-membership, the crossover point and total membership as two, four and six, respectively. According to Woodside et al. [71] the cut-off values were adjusted according to the number of elements of each variable and their statistics. EXP and EL are categorical variables that we calibrated at three levels (1, 0.5, 0), and SIZ is a binary variable that did not need calibration (it adopts the value of zero or one) (Table 2).


**Table 2.** Descriptive statistics and calibrations of outcome and causal conditions.

μ = average; σ = standard deviation; min = minimum; max = maximum; \* = (0.95; 0.50; 0.05).
