I am using fsQCA software to perform the analysis. Could somebody elaborate the steps to calibrate a 7-point Likert scale? Say my target variable name is PE. and I have selected
calibrate(x,n1,n2,n3) function. Could you please explain what arguments would come in the function?
Hi Sabraz,
you might find useful this paper:
http://www.compasss.org/wpseries/EmmeneggerSchraffWalter2014.pdf
Francesco
Set membership scores are derived from empirical and conceptual knowledge.
One method from literature to transform a five points Likert type response scale into fuzzy set scores is:
0 = Likert score 1 (strongly disagree)
0.33 = Likert score 2 (somewhat disagree)
0.67 = Likert score 3 or 4 (neither agree nor disagree, or somewhat agree)
1 = Likert score 5 (strongly agree)
In a situation where the Likert response scale is large enough (at least seven points), and the responses are more or less evenly distributed across all values, there might be a straightforward method to obtain fuzzy scores from these categorical response values.
In a study addressing the fuzzy and relative poverty measures, Cheli and
Lemmi (1995) seek to analyze poverty in a multidimensional perspective. They proposed a method called TFR (totally fuzzy and relative) based on rank orders, thus applicable to both ordinal and interval levels of measurement.
The TFR technique uses an empirical cumulative distribution function on the
observed data, and it is best suited to interval level data. However, when data is categorical (even skewed), they propose a normalized version by applying a simple transformation to create a membership function that outputs scores between 0 and 1.
The formula below is an adaptation of their function, restricted to values equal to 0 or above, to make sure it can never output negative values :
TFR = max[0, E(x)−E(1) /1−E(1)]
E() is the empirical cumulative distribution function of the observed data,
and the formula basically calculates the distance from each CDF value to the
CDF of the first value 1 in the Likert response scale, and divide that to the
distance between 1 (the maximum possible fuzzy score) to the same CDF of
the first value 1 in the same Likert response scale.
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