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Intuitionistic fuzzy sets in questionnaire analysis

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  • Donata Marasini
  • Piero Quatto
  • Enrico Ripamonti

Abstract

Fuzzy sets represent an extension of the concept of set, used to mathematically model veiled and indefinite concepts, such as those of youth, poverty, customer satisfaction and so on. Fuzzy theory introduces a membership function, expressing the degree of membership of the elements to a set. Intuitionistic fuzzy sets and hesitant fuzzy sets are two extensions of the theory of fuzzy sets, in which non-membership degrees and hesitations expressed by a set of experts are, respectively, introduced. In this paper, we apply intuitionistic fuzzy sets to questionnaire analysis, with a focus on the construction of membership, non-membership and uncertainty functions. We also suggest the possibility of considering intuitionistic hesitant fuzzy sets as a valuable theoretical framework. We apply these models to the evaluation of a Public Administration and we assess our results through a sensitivity analysis. Copyright Springer Science+Business Media Dordrecht 2016

Suggested Citation

  • Donata Marasini & Piero Quatto & Enrico Ripamonti, 2016. "Intuitionistic fuzzy sets in questionnaire analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(2), pages 767-790, March.
    • Handle: RePEc:spr:qualqt:v:50:y:2016:i:2:p:767-790
    DOI: 10.1007/s11135-015-0175-3
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    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Corrado Crocetta & Giuseppe Delvecchio, 2007. "A Fuzzy Measure of Satisfaction for University Education as a Key for Employment," Springer Books, in: Luigi Fabbris (ed.), Effectiveness of University Education in Italy, pages 11-27, Springer.
    3. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.
    4. Castillo, C. & Lorenzana, T., 2010. "Evaluation Of Business Scenarios By Means Of Composite Indicators," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 3-20, May.
    5. Soondool Chung & Hyeji Choi & Sophia Lee, 2014. "Measuring Social Capital in the Republic of Korea with Mixed Methods: Application of Factor Analysis and Fuzzy-Set Ideal Type Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 117(1), pages 45-64, May.
    6. Harvey Goldstein & Jon Rasbash & William Browne & Geoffrey Woodhouse & Michel Poulain, 2000. "Multilevel Models in the Study of Dynamic Household Structures," European Journal of Population, Springer;European Association for Population Studies, vol. 16(4), pages 373-387, December.
    7. Michelle Lalla & Gisella Facchinetti & Giovanni Mastroleo, 2005. "Ordinal scales and fuzzy set systems to measure agreement: An application to the evaluation of teaching activity," Quality & Quantity: International Journal of Methodology, Springer, vol. 38(5), pages 577-601, January.
    8. Cleriston Silva & Deise Araújo Batista & Denise Medeiros, 2014. "A proposed method to evaluate the quality of services using Fuzzy sets theory," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(2), pages 871-885, March.
    9. Gianni Betti & Antonella D’Agostino & Laura Neri, 2011. "Educational Mismatch of Graduates: a Multidimensional and Fuzzy Indicator," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 103(3), pages 465-480, September.
    10. Meimei Xia & Zeshui Xu & Na Chen, 2013. "Some Hesitant Fuzzy Aggregation Operators with Their Application in Group Decision Making," Group Decision and Negotiation, Springer, vol. 22(2), pages 259-279, March.
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    Cited by:

    1. Atiq-ur Rehman & Mustanser Hussain & Adeel Farooq & Muhammad Akram, 2019. "Consensus-Based Multi-Person Decision Making with Incomplete Fuzzy Preference Relations Using Product Transitivity," Mathematics, MDPI, vol. 7(2), pages 1-13, February.
    2. Juan Carlos Martín & Alessandro Indelicato, 2023. "A fuzzy-hybrid analysis of citizens’ perception toward immigrants in Europe," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1101-1124, April.
    3. Donata Marasini & Piero Quatto & Enrico Ripamonti, 2017. "Inferential confidence intervals for fuzzy analysis of teaching satisfaction," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(4), pages 1513-1529, July.

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