Hypothesis testing-based comparative analysis between rating scales for intrinsically imprecise data

Abstract

In previous papers, it has been empirically proved that descriptive (summary measures) and inferential conclusions (in particular, tests about means p-values) with imprecise-valued data are often affected by the scale considered to model such data. More concretely, conclusions from the numerical and fuzzy linguistic encodings of Likert-type data have been compared with those for fuzzy data obtained by using a totally free fuzzy assessment: the so-called fuzzy rating scale. These previous comparisons have been performed separately for each of the scales. This paper aims to perform a joint comparison in such a way that means of linked data (one associated with the fuzzy rating and the other one with the encoded Likert scale) are to be tested for equality. Two real-life examples, as well as several simulation-based synthetic ones, have unequivocally shown that the fuzzy rating scale means are significantly different from those for the encoded Likert scales.

Confirmatory Factor Analysis of Assets That Influence Informal Garment Workers’ Livelihood Security in Laos

“…These items were used as the observed variable loading in the latent variable of livelihood security. All of the items were graded by five options: 1 (very poor) to 5 (very good) [51]. A dummy variable was assigned the number "1" to indicate the presence of attribute and "0" the absence of attribute.…”

Estimation of maintenance costs of a pipeline for a U-shaped hazard rate function in the imprecise setting

“…, then the directional component of the descending linear part of ( 1) is described by a TPFN for which its core is given by the interval [ ] 0.6,0.65 and its support by [ ] 0.5,0.7 . Therefore, we are completely sure, that the considered value is in the interval [ ] 0.6,0.65 and also "certain to some extent", that this value is in [ ] 0.5,0.7 , so this parameter is "about 0.6.0-65 plus 0.05 /minus 0.1" (for additional remarks concerning the fuzzy scales and their interpretation, see, e.g., [15]). And for [ ] , 0.5,1, 2 R const cost = we can say, that the constant costs of a single repair are "about 1, minus 0.5 (50% of the core value) / plus 1 (100% of the core value)", so this parameter has longer right-hand support.…”

Fuzzy data analysis and classification

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