Skip to main content
SpringerLink
Log in

New Fuzzy Composite Indicators for Dyslexia

  • Conference paper
  • First Online:
Book cover New Statistical Developments in Data Science (SIS 2017)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 288))

Included in the following conference series:

  • 1156 Accesses

Abstract

Composite indicators should ideally identify multidimensional concepts that cannot be captured by a single variable. In this paper, we suggest a method based on fuzzy set theory for the construction of fuzzy synthetic indexes of dyslexia, using the set of manifest variables measured by means of reading tests. A few criteria for assigning values to the membership function are discussed, as well as criteria for defining the weights of the variables. An application regarding the diagnosis of dyslexia in primary and middle school in Italy is presented. In this application, the fuzzy approach is compared with the crisp approach actually used in Italy for detecting dyslexic children in compulsory school.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baliamoune-Lutz, M.: On the Measurement of Human Well-Being: Fuzzy Set Theory and Sen’s Capability Approach, UNU-WIDER, Helsinki (2004)

    Google Scholar 

  2. Baliamoune-Lutz, M., McGillivray, M.: Fuzzy well-being achievement in Pacific Asia. J. Asia Pac. Econ. 11, 168–177 (2006)

    Article  Google Scholar 

  3. Cerioli, A., Zani, S.: A fuzzy approach to the measurement of poverty. In: Dagum, C., Zenga, M. (eds.) Income and Wealth Distribution, Inequality and Poverty, pp. 272–284. Springer, Berlin (1990)

    Chapter  Google Scholar 

  4. Cheli, B., Lemmi, A.: A totally fuzzy and relative approach to the multidimensional analysis of poverty. Econ. Notes 24(1), 115–134 (1995)

    Google Scholar 

  5. Chiappero Martinetti, E.: A multidimensional assessment of well-being based on sen’s functioning approach. Rivista Internazionale di Scienze Sociali 108(2), 207–239 (2000)

    Google Scholar 

  6. Chien, C.-J., Tsai, H.-H.: Using fuzzy numbers to evaluate perceived service quality. Fuzzy Sets Syst. 116, 289–300 (2000)

    Article  Google Scholar 

  7. Darestani, A.Y., Jahromi, A.E.: Measuring customer satisfaction using a fuzzy inference system. J. Appl. Sci. 9(3), 469–478 (2009)

    Article  Google Scholar 

  8. Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainity and Information. Prentice-Hall Int, London (1988)

    MATH  Google Scholar 

  9. Kwong, C.K., Bai, H.: A fuzzy AHP approach to the determination of importance weights of customer requirements in quality function deployment. J. Intell. Manuf. 13, 367–377 (2002)

    Article  Google Scholar 

  10. Lazim, M.A., Osman, M.T.A.: A new Malaysian quality of life index based on fuzzy sets and hierarchical needs. Soc. Indic. Res. 94(3), 499–508 (2009)

    Article  Google Scholar 

  11. Lorusso, M.L., Vernice, M., Dieterich, M., Brizzolara, D., Mariani, E., De Masi, S., D’Angelo, F., Lacorte, E., Mele, A.: The process and criteria for diagnosing specific learning disorderes: indications from the consensus conference promoted by the Italian national institute of health. Ann. Ist. Super. Sanita 50(1), 77–89 (2014)

    Google Scholar 

  12. Morlini, I., Stella, G., Scorza, M.: A new procedure to measure children reading speed and accuracy in Italian. Dyslexia 48(2), 176–195 (2014)

    Google Scholar 

  13. Morlini, I., Stella, G., Scorza, M.: Assessing decoding ability: the role of speed and accuracy and a new composite indicator to measure decoding skill in elementary grades. J. Learn. Disabil. 20, 54–73 (2015)

    Google Scholar 

  14. Morlini, I.: New fuzzy methods for psychometric data. In: Greselin, F., Mola, F., Zenga, F. (eds.) Cladag 2017 Book of Short Papers, Universitas Studiorum. Mantova, Italy (2017)

    Google Scholar 

  15. Sartori, G., Job, R., Tressoldi, P.E.: DDE-2 battery for the evaluation of dyslexia and disorthographya. Giunti, Florence (2007)

    Google Scholar 

  16. Smithson, M., Verkuilen, J.: Fuzzy Sets Theory: Applications in the Social Sciences. Sage Publications, London (2006)

    Book  Google Scholar 

  17. World Health Organization: ICD-10: International Statistical Classification of Diseases and Related Health Problems 10th Revsion. World Health Organization, Geneve (2008)

    Google Scholar 

  18. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

  19. Zani, S., Berzieri, L.: Measuring customer satisfaction using ordinal variables: an application in a survey on a contact center. Stat. Appl. Italian J. Appl. Stat. 20(3–4), 331–351 (2008)

    Google Scholar 

  20. Zani, S., Milioli, M.A., Morlini, I.: Fuzzy methods and satisfaction indices. In: Kenett, R.S., Salini, S. (eds.) Modern Analysis of Customer Surveys, pp. 439–455. Wiley, Chichester (2012)

    Google Scholar 

  21. Zani, S., Milioli, M.A., Morlini, I.: Fuzzy composite indicators: an application for measuring customer satisfaction. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds.) Advances in Theoretical and Applied Statistics, pp. 241–251. Springer, Berlin (2013)

    Google Scholar 

  22. Zimmermann, H.J.: Fuzzy Sets Theory and its Applications, 2nd edn. Kluwer, Boston (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics Marco Biagi, University of Modena and Reggio Emilia, Viale Berengario 51, 41121, Modena, Italy

    Isabella Morlini

  2. Department of Social Sciences, University of Modena and Reggio Emilia, Viale A. Allegri 9, 42121, Reggio Emilia, Italy

    Maristella Scorza

Authors
  1. Isabella Morlini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maristella Scorza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Isabella Morlini .

Editor information

Editors and Affiliations

  1. Dipartimento di Statistica, Informatica, Applicazioni ‘G. Parenti’ (DiSIA), Università degli Studi di Firenze, Florence, Italy

    Alessandra Petrucci

  2. Dipartimento Scienze Statistiche, Sapienza Università di Roma, Rome, Italy

    Filomena Racioppi

  3. Dipartimento di Matematica e Fisica, Università della Campania ‘Luigi Vanvitelli’, Caserta, Italy

    Rosanna Verde

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Morlini, I., Scorza, M. (2019). New Fuzzy Composite Indicators for Dyslexia. In: Petrucci, A., Racioppi, F., Verde, R. (eds) New Statistical Developments in Data Science. SIS 2017. Springer Proceedings in Mathematics & Statistics, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-030-21158-5_24

Download citation

Publish with us

Policies and ethics

Search

Navigation