Abstract
Many phenomena like burnout are gauged by computing a linear combination of user-provided Likert-scale values. The problem with this traditional approach is that, while it makes sense to have linear combination of weights or other physical characteristics, a linear combination of Likert-scale values like “good” and “satisfactory” does not make sense. The only reason why linear combinations are used in practice is that the corresponding data processing tools are readily available. A more adequate approach would be to use fuzzy logic – a technique specifically designed to deal with Likert-scale values. We show that fuzzy logic actually leads to a linear combination – but not of the original degrees, but of their transformed values. The corresponding transformation function – as well as the coefficients of the corresponding linear combination – must be determined from the condition that the resulting expression best fits the available data.
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Acknowledgments
This work was supported in part by the National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science), and HRD-1834620 and HRD-2034030 (CAHSI Includes). It was also supported by the program of the development of the Scientific-Educational Mathematical Center of Volga Federal District No. 075-02-2020-1478.
The authors are thankful to the anonymous referees for valuable suggestions.
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Zapata, F., Kosheleva, O., Kreinovich, V. (2022). Fuzzy Logic Leads to a More Adequate Way of Processing Likert-Scale Values: Case Study of Burnout. In: Rayz, J., Raskin, V., Dick, S., Kreinovich, V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_45
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DOI: https://doi.org/10.1007/978-3-030-82099-2_45
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