IDEAS home Printed from https://ideas.repec.org/a/oup/scippl/v34y2007i8p555-563.html
   My bibliography  Save this article

Evaluating universities using simple scientometric research-output metrics: Total citation counts per university for a retrospective seven-year rolling sample

Author

Listed:
  • Bruce G Charlton
  • Peter Andras

Abstract

We advocate a scientometric, top-down and institution-based research-assessment methodology that is based on total citations accumulated from all publications associated with a specific university during the survey period. The exercise could be done every year using a rolling seven-year retrospective sample and should be performed by at least two independent auditors. Identification of elite ‘revolutionary-science’ institutions could be accomplished using a metric derived from the distribution of science Nobel Prizes. Copyright , Beech Tree Publishing.

Suggested Citation

  • Bruce G Charlton & Peter Andras, 2007. "Evaluating universities using simple scientometric research-output metrics: Total citation counts per university for a retrospective seven-year rolling sample," Science and Public Policy, Oxford University Press, vol. 34(8), pages 555-563, October.
    • Handle: RePEc:oup:scippl:v:34:y:2007:i:8:p:555-563
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.3152/030234207X254413
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

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


    Cited by:

    1. Goodall, Amanda H., 2009. "Highly cited leaders and the performance of research universities," Research Policy, Elsevier, vol. 38(7), pages 1079-1092, September.
    2. Hyeonchae Yang & Woo-Sung Jung, 2015. "A strategic management approach for Korean public research institutes based on bibliometric investigation," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1437-1464, July.
    3. Wai Ching Poon & Gareth D. Leeves, 2017. "Is there gender gap unequivocally? Evidence from research output 1958–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1687-1701, June.
    4. Liang Zhang & Wei Bao & Liang Sun, 2016. "Resources and Research Production in Higher Education: A Longitudinal Analysis of Chinese Universities, 2000–2010," Research in Higher Education, Springer;Association for Institutional Research, vol. 57(7), pages 869-891, November.
    5. José Manuel Pastor & Lorenzo Serrano & Irene Zaera, 2015. "The research output of European higher education institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 1867-1893, March.
    6. Jane Payumo & Taurean Sutton & Derek Brown & Dan Nordquist & Marc Evans & Danna Moore & Prema Arasu, 2017. "Input–output analysis of international research collaborations: a case study of five U.S. universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1657-1671, June.
    7. João Carlos Nabout & Fabrício Barreto Teresa & Karine Borges Machado & Vitor Hugo Mendonça Prado & Luis Mauricio Bini & José Alexandre Felizola Diniz-Filho, 2018. "Do traditional scientometric indicators predict social media activity on scientific knowledge? An analysis of the ecological literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 1007-1015, May.
    8. Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan & Medo, Matúš, 2020. "Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data," Journal of Informetrics, Elsevier, vol. 14(1).
    9. Hamid Bouabid & Mohamed Dalimi & Zayer ElMajid, 2011. "Impact evaluation of the voluntary early retirement policy on research and technology outputs of the faculties of science in Morocco," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 125-132, January.
    10. Thelwall, Mike, 2016. "The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression," Journal of Informetrics, Elsevier, vol. 10(2), pages 336-346.

    More about this item

    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:oup:scippl:v:34:y:2007:i:8:p:555-563. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/spp .

    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.