IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v97y2013i3d10.1007_s11192-013-1038-0.html
   My bibliography  Save this article

An empirical approach to compare the performance of heterogeneous academic fields

Author

Listed:
  • Giancarlo Ruocco

    (University of Rome ‘La Sapienza’)

  • Cinzia Daraio

    (University of Rome ‘La Sapienza’)

Abstract

In this paper, we propose a ‘scaling’ approach to compare the scientific performance of Italian heterogeneous academic disciplines. This method is based on the idea that, after eliminating the percentages of ‘silent’ researchers, the distribution of bibliometric parameters of the different academic fields can be superimposed and collapse into a unique master curve by a single scaling parameter. By using data on the scientific production of around 2,500 scholars of the university of Rome ‘La Sapienza’ from the Web of Science from 2004 to 2008, we (i) demonstrate the existence of a master curve, (ii) determine the scaling factors that work like rates of substitution to compare the scientific production across different academic fields on a common ground, (iii) show that the master bibliometric distribution follows a log-normal law and (iv) illustrate the relevance of the proposed approach for research assessment and allocation of competitive funding at the university level.

Suggested Citation

  • Giancarlo Ruocco & Cinzia Daraio, 2013. "An empirical approach to compare the performance of heterogeneous academic fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 601-625, December.
    • Handle: RePEc:spr:scient:v:97:y:2013:i:3:d:10.1007_s11192-013-1038-0
    DOI: 10.1007/s11192-013-1038-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-013-1038-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-013-1038-0?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Katz, J. Sylvan, 1999. "The self-similar science system1," Research Policy, Elsevier, vol. 28(5), pages 501-517, June.
    2. Rodrigo Costas & Thed N. van Leeuwen & María Bordons, 2010. "A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(8), pages 1564-1581, August.
    3. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    4. Pedro Albarrán & Juan A. Crespo & Ignacio Ortuño & Javier Ruiz-Castillo, 2011. "The skewness of science in 219 sub-fields and a number of aggregates," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 385-397, August.
    5. Daraio, Cinzia & Bonaccorsi, Andrea & Geuna, Aldo & Lepori, Benedetto & Bach, Laurent & Bogetoft, Peter & F. Cardoso, Margarida & Castro-Martinez, Elena & Crespi, Gustavo & de Lucio, Ignacio Fernandez, 2011. "The European university landscape: A micro characterization based on evidence from the Aquameth project," Research Policy, Elsevier, vol. 40(1), pages 148-164, February.
    6. J Sylvan Katz, 2000. "Scale-independent indicators and research evaluation," Science and Public Policy, Oxford University Press, vol. 27(1), pages 23-36, February.
    7. T. S. Evans & N. Hopkins & B. S. Kaube, 2012. "Universality of performance indicators based on citation and reference counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 473-495, November.
    8. Rodrigo Costas & Thed N. van Leeuwen & María Bordons, 2010. "A bibliometric classificatory approach for the study and assessment of research performance at the individual level: The effects of age on productivity and impact," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(8), pages 1564-1581, August.
    9. Daraio, Cinzia & Moed, Henk F., 2011. "Is Italian science declining?," Research Policy, Elsevier, vol. 40(10), pages 1380-1392.
    10. Wolfgang G. Stock, 2006. "On relevance distributions," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(8), pages 1126-1129, June.
    11. Anthony F. J. Van Raan, 2001. "Competition amongst scientists for publication status:Toward a model of scientific publication and citation distributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(1), pages 347-357, April.
    12. Ludo Waltman & Nees Jan van Eck & Anthony F. J. van Raan, 2012. "Universality of citation distributions revisited," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 72-77, January.
    13. Enrico Deiaco & Alan Hughes & Maureen McKelvey, 2012. "Universities as strategic actors in the knowledge economy," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 36(3), pages 525-541.
    14. Anthony F. J. Raan, 2006. "Comparison of the Hirsch-index with standard bibliometric indicators and with peer judgment for 147 chemistry research groups," Scientometrics, Springer;Akadémiai Kiadó, vol. 67(3), pages 491-502, June.
    15. Per O. Seglen, 1992. "The skewness of science," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 43(9), pages 628-638, October.
    16. Wolfgang Glänzel, 2010. "The role of the h-index and the characteristic scores and scales in testing the tail properties of scientometric distributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 697-709, June.
    17. Ludo Waltman & Nees Jan van Eck & Anthony F. J. van Raan, 2012. "Universality of citation distributions revisited," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 72-77, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Achraf Haddad & Anis El Ammari & Abdelfattah Bouri, 2020. "Comparative and Demonstrative Study Between the Liquidity of Islamic and Conventional Banks in a Financial Stability Period: Which Type of Banks Is the Most Liquid?," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(1), pages 252-273, January.
    2. Diniz-Filho, José Alexandre F. & Fioravanti, Maria Clorinda S. & Bini, Luis Mauricio & Rangel, Thiago Fernando, 2016. "Drivers of academic performance in a Brazilian university under a government-restructuring program," Journal of Informetrics, Elsevier, vol. 10(1), pages 151-161.
    3. Fabio Zagonari, 2019. "Scientific Production and Productivity for Characterizing an Author’s Publication History: Simple and Nested Gini’s and Hirsch’s Indexes Combined," Publications, MDPI, vol. 7(2), pages 1-30, May.
    4. R. Álvarez & E. Cahué & J. Clemente-Gallardo & A. Ferrer & D. Íñiguez & X. Mellado & A. Rivero & G. Ruiz & F. Sanz & E. Serrano & A. Tarancón & Y. Vergara, 2015. "Analysis of academic productivity based on Complex Networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 651-672, September.
    5. Andrea Bonaccorsi & Cinzia Daraio & Stefano Fantoni & Viola Folli & Marco Leonetti & Giancarlo Ruocco, 2017. "Do social sciences and humanities behave like life and hard sciences?," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 607-653, July.

    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. Cinzia Daraio & Giancarlo Ruocco, 2012. "An Empirical Approach to Compare the Performance of Heterogeneous Academic Fields," DIAG Technical Reports 2012-03, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    2. Ruiz-Castillo, Javier & Costas, Rodrigo, 2014. "The skewness of scientific productivity," Journal of Informetrics, Elsevier, vol. 8(4), pages 917-934.
    3. Zhihui Zhang & Ying Cheng & Nian Cai Liu, 2015. "Improving the normalization effect of mean-based method from the perspective of optimization: optimization-based linear methods and their performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 587-607, January.
    4. Javier Ruiz-Castillo, 2013. "The role of statistics in establishing the similarity of citation distributions in a static and a dynamic context," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 173-181, July.
    5. S. R. Goldberg & H. Anthony & T. S. Evans, 2015. "Modelling citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1577-1604, December.
    6. Andrea Bonaccorsi & Cinzia Daraio & Stefano Fantoni & Viola Folli & Marco Leonetti & Giancarlo Ruocco, 2017. "Do social sciences and humanities behave like life and hard sciences?," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 607-653, July.
    7. Zhihui Zhang & Ying Cheng & Nian Cai Liu, 2014. "Comparison of the effect of mean-based method and z-score for field normalization of citations at the level of Web of Science subject categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1679-1693, December.
    8. Vîiu, Gabriel-Alexandru, 2017. "Disaggregated research evaluation through median-based characteristic scores and scales: a comparison with the mean-based approach," Journal of Informetrics, Elsevier, vol. 11(3), pages 748-765.
    9. Ruiz-Castillo, Javier & Costas, Rodrigo, 2018. "Individual and field citation distributions in 29 broad scientific fields," Journal of Informetrics, Elsevier, vol. 12(3), pages 868-892.
    10. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2012. "How important is choice of the scaling factor in standardizing citations?," Journal of Informetrics, Elsevier, vol. 6(4), pages 645-654.
    11. Ruiz-Castillo, Javier & Waltman, Ludo, 2015. "Field-normalized citation impact indicators using algorithmically constructed classification systems of science," Journal of Informetrics, Elsevier, vol. 9(1), pages 102-117.
    12. Vîiu, Gabriel-Alexandru, 2018. "The lognormal distribution explains the remarkable pattern documented by characteristic scores and scales in scientometrics," Journal of Informetrics, Elsevier, vol. 12(2), pages 401-415.
    13. Thelwall, Mike & Wilson, Paul, 2014. "Distributions for cited articles from individual subjects and years," Journal of Informetrics, Elsevier, vol. 8(4), pages 824-839.
    14. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    15. Albarrán, Pedro & Ortuño, Ignacio & Ruiz-Castillo, Javier, 2011. "The measurement of low- and high-impact in citation distributions: Technical results," Journal of Informetrics, Elsevier, vol. 5(1), pages 48-63.
    16. T. S. Evans & N. Hopkins & B. S. Kaube, 2012. "Universality of performance indicators based on citation and reference counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 473-495, November.
    17. Bouyssou, Denis & Marchant, Thierry, 2016. "Ranking authors using fractional counting of citations: An axiomatic approach," Journal of Informetrics, Elsevier, vol. 10(1), pages 183-199.
    18. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "On the interplay between normalisation, bias, and performance of paper impact metrics," Journal of Informetrics, Elsevier, vol. 13(1), pages 270-290.
    19. Pedro Albarrán & Juan A. Crespo & Ignacio Ortuño & Javier Ruiz-Castillo, 2011. "The skewness of science in 219 sub-fields and a number of aggregates," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 385-397, August.
    20. Tol, Richard S.J., 2013. "The Matthew effect for cohorts of economists," Journal of Informetrics, Elsevier, vol. 7(2), pages 522-527.

    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:spr:scient:v:97:y:2013:i:3:d:10.1007_s11192-013-1038-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.