Application of Rating Scale Model in Conversion of Rating Scales' Points to the Form of Triangular Fuzzy Numbers

• Autorzy: Bartłomiej Jefmański
• Języki publikacji: EN

Abstrakty

EN

A new application of fuzzy sets theory in social and economic research is a fuzzy measurement of respondents' opinions. In the subject literature fuzzy rating scales or fuzzy conversion scales are being applied. In this second case, a key stage is a choice of such parameters' values of fuzzy numbers which will best illustrate the perception of linguistic values constituting points of measurement scales. In the construction of fuzzy conversion scales the item response theory models can find an application. The transformation method of verbal categories to the form of triangular fuzzy numbers with the application of rating scale model was proposed in this article. Usefulness of a suggested approach was introduced on the basis of the analysis of selected research results on inhabitants' quality of life in one of the Lower Silesian Voivodship districts. The analysis results showed big ambiguity of particular verbal categories and, in consequence, the validity of fuzzy conversion scales application.(original abstract)

Słowa kluczowe

EN

Fuzzy systems; Fuzzy sets; Quality of life

PL

Systemy rozmyte; Zbiory rozmyte; Jakość życia

• Czasopismo: Folia Oeconomica Stetinensia
• Rocznik: 2014
• Tom: 14
• Numer: nr 2
• Strony: 7--18

Twórcy

autor

Bartłomiej Jefmański

• Wrocław University of Economics, Poland

Bibliografia

• Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561-573. DOI: 10.1007/BF02293814.
• Bojadziev, M. & Bojadziev, G. (1995). Fuzzy Sets, Fuzzy Logic, Applications. Singapore: World Scientific Publishing.
• Carrasco, R.A., Munoz-Leiva, F., Sánchez-Fernández, J. & Liébana-Cabanillas, F.J. (2012). A model for the integration of e-financial services questionnaires with SERVQUAL scales under fuzzy linguistic modeling. Expert Systems with Applications, 39, 1535-1547. DOI: 10.1016/j.eswa.2012.03.055.
• Charles, V., Kumar, M. & Suggu, S. (2013). Adapting Fuzzy Linguistic Servqual Model: A Comparative Analysis of Bank Services. Middle-East Journal of Scientific Research, 18 (8), 1119-1132. DOI: http://dx.doi.org/10.7835/ccwp-2012-09-0008.
• Chien, Ch.-J. & Tsai, H.-H. (2000). Using fuzzy number to evaluate perceived service quality. Fuzzy Sets and Systems, 116 (2), 289-300. DOI: 10.1016/S0165-0114(98)00239-5.
• DeMars, Ch. (2010). Item Response Theory. Oxford: Oxford University Press.
• de Sáa, S.R., Gil, M.Á., Garcia, M.T.L. & Lubiano, M.A. (2013). Fuzzy Rating vs. Fuzzy Conversion Scales: An Empirical Comparison through the MSE. In: Synergies of Soft Computing and Statistics for Intelligent Data Analysis, eds. R. Kruse, M.R. Berthold, M. Moewes, M.A. Gil, P. Grzegorzewski, O. Hryniewicz (pp. 135-144). Berlin Heidelberg: Springer.
• Embretson, S.E. & Reise, S.P. (2000). Item Response Theory for Psychologists. Makwah: Lawrence Erlbaum Associates.
• Erdoğan, M., Bilişik, Ö.N., Kaya, İ. & Baraçh, H. (2013). A customer satisfaction model based on fuzzy TOPSIS and SERVQUAL methods. Lecture Notes in Management Science, 5, 74-83. DOI: 10.1080/14783363.2013.809942.
• Farkhondezadeh, A., Rakhsha, S.H., Fek, M.R., Zarafshan, H., Cheramy, F. & Yahdy, E. (2013). Identification and ranking of effective factors of marketing (controllable) to receive the services from free zone with MADM approach. European Online Journal of Natural and Social Sciences, 2 (3), 507-517.
• Hambleton, R.K., Swaminathan, H. & Rogers, H.J. (1991). Fundamentals of Item Response Theory. Newbury Park, CA: Sage Publications.
• Jefmański, B. (2011). Nowe podejście w pomiarze opinii respondentów z zastosowaniem skal porządkowych i elementów teorii zbiorów rozmytych - charakterystyka wybranych aspektów metodologicznych. Prace Naukowe UE we Wrocławiu, 236, 184-191.
• Kandel, A. (1982). Fuzzy techniques in pattern recognition. New York: John Wiley & Sons.
• Linacre, J.M. (2010). Transitional categories and usefully disordered thresholds. Online Educational Research Journal, 1-10.
• Liu, X., Zeng, X., Xu, Y. & Koehl, L. (2008). A fuzzy model of customer satisfaction index in e-commerce. Mathematics and Computers in Simulation, 77 (5-6), 512-521. DOI: 10.1016/j.matcom.2007.11.017.
• Ostini, R. & Nering, M. (2006). Polytomous Item Response Theory Models. Thousand Oaks: Sage Publications.
• Pagani, L. & Zanarotti, M.C. (2010). Some Uses of Rasch Models Parameters in Customer Satisfaction Data Analysis. Quality Technology & Quantitative Management, 7 (1), 83-95.
• Rasch, G. (1960). Probabilistic Models for Some Intelligence and Attainment Tests. Copenhagen: Danish Institute for Educational Research (Expanded edition, University of Chicago Press, 1980).
• Sagan, A. (2005). Ocena ekwiwalencji skal pomiarowych w badaniach międzykulturowych. Zeszyty Naukowe Akademii Ekonomicznej w Krakowie, 659, 59-73.
• Zimmermann, H.-J. (2001). Fuzzy set theory and its applications. Boston: Kluwer Academic Publishers.

Identyfikatory

DOI

10.1515/foli-2015-0010