IDEAS home Printed from https://ideas.repec.org/a/vrs/foeste/v14y2014i2p7-18n10.html
   My bibliography  Save this article

Application Of Rating Scale Model In Conversion Of Rating Scales' Points To The Form Of Triangular Fuzzy Numbers

Author

Listed:
  • Jefmański Bartłomiej

    (Wrocław University of Economics, Faculty of Economics, Management and Tourism, Department of Econometrics and Computer Science, Nowowiejska 3, 58-500 Jelenia Góra, Poland)

Abstract

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.

Suggested Citation

  • Jefmański Bartłomiej, 2014. "Application Of Rating Scale Model In Conversion Of Rating Scales' Points To The Form Of Triangular Fuzzy Numbers," Folia Oeconomica Stetinensia, Sciendo, vol. 14(2), pages 7-18, December.
  • Handle: RePEc:vrs:foeste:v:14:y:2014:i:2:p:7-18:n:10
    DOI: 10.1515/foli-2015-0010
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/foli-2015-0010
    Download Restriction: no

    File URL: https://libkey.io/10.1515/foli-2015-0010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. David Andrich, 1978. "A rating formulation for ordered response categories," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 561-573, December.
    2. Liu, Xiaohong & Zeng, Xianyi & Xu, Yang & Koehl, Ludovic, 2008. "A fuzzy model of customer satisfaction index in e-commerce," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 77(5), pages 512-521.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eun-Young Park & Soojung Chae, 2020. "Rasch Analysis of the Korean Parenting Stress Index Short Form (K-PSI-SF) in Mothers of Children with Cerebral Palsy," IJERPH, MDPI, vol. 17(19), pages 1-11, September.
    2. P. A. Ferrari & S. Salini, 2008. "Measuring Service Quality: The Opinion of Europeans about Utilities," Working Papers 2008.36, Fondazione Eni Enrico Mattei.
    3. Chang, Hsin-Li & Yang, Cheng-Hua, 2008. "Explore airlines’ brand niches through measuring passengers’ repurchase motivation—an application of Rasch measurement," Journal of Air Transport Management, Elsevier, vol. 14(3), pages 105-112.
    4. Ivana Bassi & Matteo Carzedda & Enrico Gori & Luca Iseppi, 2022. "Rasch analysis of consumer attitudes towards the mountain product label," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-25, December.
    5. Antonio Caronni & Marina Ramella & Pietro Arcuri & Claudia Salatino & Lucia Pigini & Maurizio Saruggia & Chiara Folini & Stefano Scarano & Rosa Maria Converti, 2023. "The Rasch Analysis Shows Poor Construct Validity and Low Reliability of the Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 (QUEST 2.0) Questionnaire," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    6. Wanke, Peter Fernandes & Chiappetta Jabbour, Charbel José & Moreira Antunes, Jorge Junio & Lopes de Sousa Jabbour, Ana Beatriz & Roubaud, David & Sobreiro, Vinicius Amorim & Santibanez Gonzalez‬, Erne, 2021. "An original information entropy-based quantitative evaluation model for low-carbon operations in an emerging market," International Journal of Production Economics, Elsevier, vol. 234(C).
    7. Hua-Hua Chang, 1996. "The asymptotic posterior normality of the latent trait for polytomous IRT models," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 445-463, September.
    8. Curt Hagquist & Raili Välimaa & Nina Simonsen & Sakari Suominen, 2017. "Differential Item Functioning in Trend Analyses of Adolescent Mental Health – Illustrative Examples Using HBSC-Data from Finland," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 10(3), pages 673-691, September.
    9. Wang, Luming & Finn, Adam, 2014. "A psychometric theory that measures up to marketing reality: An adapted Many Faceted IRT model," Australasian marketing journal, Elsevier, vol. 22(2), pages 93-102.
    10. Qiu-Yue Zhong & Bizu Gelaye & Alan M Zaslavsky & Jesse R Fann & Marta B Rondon & Sixto E Sánchez & Michelle A Williams, 2015. "Diagnostic Validity of the Generalized Anxiety Disorder - 7 (GAD-7) among Pregnant Women," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-17, April.
    11. Cristante, Francesca & Robusto, Egidio, 1999. "Assessing dependence among subjects' responses," Mathematical Social Sciences, Elsevier, vol. 38(3), pages 259-274, November.
    12. Amy Snyder & Kenneth Royal, 2016. "Investigating the Financial Awareness and Behaviors of Veterinary Medical Students," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(7), pages 201-201, July.
    13. Nicole Gideon & Nick Hawkes & Jonathan Mond & Rob Saunders & Kate Tchanturia & Lucy Serpell, 2016. "Development and Psychometric Validation of the EDE-QS, a 12 Item Short Form of the Eating Disorder Examination Questionnaire (EDE-Q)," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-19, May.
    14. Huang, Jen-Hung & Peng, Kua-Hsin, 2012. "Fuzzy Rasch model in TOPSIS: A new approach for generating fuzzy numbers to assess the competitiveness of the tourism industries in Asian countries," Tourism Management, Elsevier, vol. 33(2), pages 456-465.
    15. Geofferey Masters & Benjamin Wright, 1984. "The essential process in a family of measurement models," Psychometrika, Springer;The Psychometric Society, vol. 49(4), pages 529-544, December.
    16. Salzberger, Thomas & Newton, Fiona J. & Ewing, Michael T., 2014. "Detecting gender item bias and differential manifest response behavior: A Rasch-based solution," Journal of Business Research, Elsevier, vol. 67(4), pages 598-607.
    17. Karen M. Conrad & Kendon J. Conrad & Lora L. Passetti & Rodney R. Funk & Michael L. Dennis, 2015. "Validation of the Full and Short-Form Self-Help Involvement Scale Against the Rasch Measurement Model," Evaluation Review, , vol. 39(4), pages 395-427, August.
    18. Rasmus A. X. Persson, 2023. "Theoretical evaluation of partial credit scoring of the multiple-choice test item," METRON, Springer;Sapienza Università di Roma, vol. 81(2), pages 143-161, August.
    19. Wendy L. Martin & Alexander McKelvie & G. T. Lumpkin, 2016. "Centralization and delegation practices in family versus non-family SMEs: a Rasch analysis," Small Business Economics, Springer, vol. 47(3), pages 755-769, October.
    20. Chang, Hsin-Li & Wu, Shun-Cheng, 2008. "Exploring the vehicle dependence behind mode choice: Evidence of motorcycle dependence in Taipei," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(2), pages 307-320, February.

    More about this item

    Keywords

    fuzzy conversion scales; fuzzy numbers; rating scale model;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D70 - Microeconomics - - Analysis of Collective Decision-Making - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:foeste:v:14:y:2014:i:2:p:7-18:n:10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.