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University League Tables

Methodological Options for Ranking Systems: Censis Approach and Alternatives

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Statistical Methods for the Evaluation of University Systems

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

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Abstract

Since 2000, the Italian Censis research institute has compiled, on behalf of La Repubblica newspaper, the Grande Guida all’Università, a report which ranks Italian universities and faculties according to their quality. With the 2008 publication, devoted to students enrolled on degree courses in 2008–2009, the Guida has gone into its ninth edition.

In the Italian version the title “Arbitro, c’è rigore?” played upon words and with the meaning of the noun “arbitro” (referee and arbiter) and “rigore” (severity and penalty): “Arbiter, is there any penalty?”

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Notes

  1. 1.

    Censis evaluates universities along four dimensions: services, study grants, facilities, and website. Faculties are assessed by means of composite indicators of five areas. It should be borne in mind that the university league table does not depend on the results obtained by faculties, and vice versa.

  2. 2.

    Censis calls “family” each “area” of the university system.

  3. 3.

    A definition of simple and composite indicators follows.

  4. 4.

    An interesting alternative would be the use of the theoretical maximum and minimum with the simple indicators for which such values are determinable: this technique would make it possible to reduce the distances among units observed in terms of residuals among normalized values of the simple indicators.

  5. 5.

    Students “in corso” have fulfilled their examination requirements within the scheduled deadlines, whilst students “fuori corso” are still attending university beyond the duration of their courses because they have not yet completed their examination requirements.

  6. 6.

    Because P5 was furnished by Censis as a rate, it was not possible to derive the value of n 2. The score for P was obtained as the simple arithmetic mean of the simple indicators.

  7. 7.

    In the case of the educational delivery, the weighting used by Censis raises obvious questions concerning the weights (it is not stated whether specific choices were made) because the denominator of the formula should be 3.5 instead of 4. For the sake of consistency, we decided to keep the formula applied by Censis.

  8. 8.

    Likewise the case of the educational delivery, in the academic staff profile the denominator should be 2 and not 3. For the sake of consistency, we decided to keep the formula applied by Censis.

  9. 9.

    The correlation between D 2 and D 3 is negative because in the Italian university system who offer a higher number of courses usually has a minor tenured academic staff.

  10. 10.

    The correlation between PD 1 and PD 4 is negative because a higher average age of tenured academic staff implies that the same staff taught the majority of the courses (untenured “extra-academic” staff usually taught a minor number of courses).

References

  1. Agresti A, Finlay B (1997) Statistical methods for the social sciences. Prentice-Hall Inc, Englewood Cliffs, NJ

    Google Scholar 

  2. Aiello F, Attanasio M (2004) How to transform a batch of simple indicators to make up a unique one? Atti del Convegno SIS giugno 2004, Bari – Sessioni Specializzate

    Google Scholar 

  3. Aiello F, Attanasio M (2006) Some issues in constructing composite indicators. VIII international meeting on quantitative methods for applied sciences, Certosa di Pontignano, 11–13 settembre 2006

    Google Scholar 

  4. Aiello F, Librizzi L (2006) Gli indicatori nelle scienze sociali: dal qualitative al quantitativo. In: Diamond I, Jefferies J (eds) Introduzione alla statistica per le scienze sociali. Mc Graw-Hill, Milano

    Google Scholar 

  5. Allegra FS, La Rocca A (2004) Sintetizzare misure elementari: una sperimentazione di alcuni criteri per la defnizione di un indice composto. Istat, Roma

    Google Scholar 

  6. Attanasio M, Capursi V (1997) Graduatorie sulla qualità della vita: prime analisi di sensibilità delle tecniche adottate. Atti XXXV Riunione Scientifica SIEDS, Alghero

    Google Scholar 

  7. Bernardi L, Capursi V, Librizzi L (2004) Measurement awareness: the use of indicators between expectations and opportunities, SIS: Sezione Specializzata. Atti della XLIII Riunione Scientifica, Bari

    Google Scholar 

  8. Censis (2000) Grande guida all’università, La Repubblica, Milano

    Google Scholar 

  9. Censis (2001) Grande guida all’università, La Repubblica, Milano

    Google Scholar 

  10. Censis (2006) Grande guida all’università, La Repubblica, Milano

    Google Scholar 

  11. Censis (2007) Grande guida all’università, La Repubblica, Milano

    Google Scholar 

  12. Censis (2008) Grande guida all’università, La Repubblica, Milano

    Google Scholar 

  13. Corbetta PG (1999) Metodologia e tecniche della ricerca sociale. Il Mulino, Bologna

    Google Scholar 

  14. Delvecchio F (1995) Scale di misura e indicatori sociali. Cacucci Editore, Bari

    Google Scholar 

  15. Fagot RF (1994) An ordinal coefficient of relational agreement for multiple judges. Psychometrika 59(2):241–251

    Article  Google Scholar 

  16. Freudenberg M (2003) Composite Indicators of Country Performance: A Critical Assessment, OECD Science, Technology and Industry Working Papers 2003/16, OECD, Directorate for Science, Technology and Industry

    Google Scholar 

  17. Kendall MG (1962) Rank correlation methods. C. Griffin & Co. Ltd, London

    Google Scholar 

  18. Land KC (1971) On the definition of social indicators. Am Sociol 6:322

    Google Scholar 

  19. Munda G, Nardo M (2005) Constructing consistent composite indicators: the issue of weights. Institute for the Protection and Security of the Citizen, Luxemburg

    Google Scholar 

  20. Nardo M, Saisana M, Saltelli A, Tarantola S (2005a) Handbook on constructing composite indicators: methodology and user guide. OECD statistics working papers, 2005/3. OECD Publishing, Paris

    Book  Google Scholar 

  21. Nardo M, Saisana M, Saltelli A, Tarantola S (2005b) Tools for composite indicators building. European Commission-Joint Research Centre, Ispra, Italy

    Google Scholar 

  22. Saisana M, Tarantola S (2002) State-of-the-art report on current methodologies and practices for composite indicator development. European Commission-Joint Research Centre, Ispra, Italy

    Google Scholar 

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Correspondence to L. Bernardi .

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Bernardi, L., Bolzonello, P., Tuzzi, A. (2011). University League Tables. In: Attanasio, M., Capursi, V. (eds) Statistical Methods for the Evaluation of University Systems. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2375-2_3

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