IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v119y2019i2d10.1007_s11192-019-03078-z.html
   My bibliography  Save this article

Scientific Quality Index: a composite size-independent metric compared with h-index for 480 medical researchers

Author

Listed:
  • W. Pluskiewicz

    (Medical University of Silesia)

  • B. Drozdzowska

    (Medical University of Silesia)

  • P. Adamczyk

    (Medical University of Silesia)

  • K. Noga

    (Main Library, Medical University of Silesia)

Abstract

The goal of this study was to measure the scientific output of 480 authors—leaders in 12 selected branches of medicine, using the Hirsch index (the h-index) and a newly proposed Scientific Quality Index (SQI). Data were collected from the Scopus database (2008–2017) and scientific output assessments, by the h-index were compared with those by SQI. SQI is calculated by the addition of the percentage of papers cited ≥ 10 times and the mean citation score (excluding self-citations and the citations of all co-authors for both). The following mean values of basic bibliometric parameters were obtained in the whole study group: the citation index: 7250 ± 7817, the total number of papers: 187 ± 104, the total number of cited papers: 175 ± 101, the number of papers cited at least 10 times: 110 ± 75, the percent of papers cited at least 10 times: 51 ± 16, the mean number of citations per paper: 28 ± 21. The mean value of the h-index was 33.2 ± 16.1 and the mean SQI was 78.5 ± 33.4. When ranked, according to the SQI, 279 (58.1%) authors decreased, while 199 (41.5%) improved their ranking position in comparison to the h-index scores. When correlated with the basic bibliometric parameters, SQI was less dependent on both the number of publications and the number of citations in comparison with the h-index. The SQI was strongly influenced by the mean citation score, followed by the percent of papers cited ≥ 10 times. The h-index correlated with the SQI, r = 0.73, p

Suggested Citation

  • W. Pluskiewicz & B. Drozdzowska & P. Adamczyk & K. Noga, 2019. "Scientific Quality Index: a composite size-independent metric compared with h-index for 480 medical researchers," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 1009-1016, May.
  • Handle: RePEc:spr:scient:v:119:y:2019:i:2:d:10.1007_s11192-019-03078-z
    DOI: 10.1007/s11192-019-03078-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-019-03078-z
    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/s11192-019-03078-z?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. Abramo, Giovanni & D’Angelo, Ciriaco Andrea, 2016. "A farewell to the MNCS and like size-independent indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 646-651.
    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. Rodríguez-Navarro, Alonso & Brito, Ricardo, 2018. "Technological research in the EU is less efficient than the world average. EU research policy risks Europeans’ future," Journal of Informetrics, Elsevier, vol. 12(3), pages 718-731.
    2. Abramo, Giovanni & Aksnes, Dag W. & D’Angelo, Ciriaco Andrea, 2020. "Comparison of research performance of Italian and Norwegian professors and universities," Journal of Informetrics, Elsevier, vol. 14(2).
    3. Leporia, Benedetto & Geuna, Aldo & Mira, Antonietta, 2018. "Scientific Output of US and European Universities Scales Super-linearly with Resources," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201806, University of Turin.
    4. Loizides, Orestis-Stavros & Koutsakis, Polychronis, 2017. "On evaluating the quality of a computer science/computer engineering conference," Journal of Informetrics, Elsevier, vol. 11(2), pages 541-552.
    5. Albarrán, Pedro & Herrero, Carmen & Ruiz-Castillo, Javier & Villar, Antonio, 2017. "The Herrero-Villar approach to citation impact," Journal of Informetrics, Elsevier, vol. 11(2), pages 625-640.
    6. Alireza Abbasi & Mahdi Jalili & Abolghasem Sadeghi-Niaraki, 2018. "Influence of network-based structural and power diversity on research performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 579-590, October.
    7. Domingo Docampo & Jean-Jacques Bessoule, 2019. "A new approach to the analysis and evaluation of the research output of countries and institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 1207-1225, May.
    8. Koski, Timo & Sandström, Erik & Sandström, Ulf, 2016. "Towards field-adjusted production: Estimating research productivity from a zero-truncated distribution," Journal of Informetrics, Elsevier, vol. 10(4), pages 1143-1152.
    9. Camil Demetrescu & Irene Finocchi & Andrea Ribichini & Marco Schaerf, 2020. "On bibliometrics in academic promotions: a case study in computer science and engineering in Italy," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2207-2228, September.
    10. Giovanni Abramo & Les Oxley, 2021. "Scientometric‐based analysis in business and economics: Introduction, examples, and guidelines," Journal of Economic Surveys, Wiley Blackwell, vol. 35(5), pages 1261-1270, December.
    11. Giovanni Abramo & Ciriaco Andrea D’Angelo & Francesco Rosati, 2016. "The north–south divide in the Italian higher education system," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2093-2117, December.
    12. Giovanni Abramo & Ciriaco Andrea D’Angelo & Anastasiia Soldatenkova, 2017. "How long do top scientists maintain their stardom? An analysis by region, gender and discipline: evidence from Italy," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 867-877, February.
    13. Klaus Wohlrabe & Felix de Moya Anegon & Lutz Bornmann, 2018. "How efficiently produce elite US universities highly cited papers? A case study based on input and output data," ifo Working Paper Series 264, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    14. Abramo, Giovanni, 2018. "Revisiting the scientometric conceptualization of impact and its measurement," Journal of Informetrics, Elsevier, vol. 12(3), pages 590-597.
    15. Abramo, Giovanni & D’Angelo, Ciriaco Andrea, 2016. "A comparison of university performance scores and ranks by MNCS and FSS," Journal of Informetrics, Elsevier, vol. 10(4), pages 889-901.
    16. Dag W. Aksnes & Liv Langfeldt & Paul Wouters, 2019. "Citations, Citation Indicators, and Research Quality: An Overview of Basic Concepts and Theories," SAGE Open, , vol. 9(1), pages 21582440198, February.
    17. Lutz Bornmann & Klaus Wohlrabe & Felix Moya Anegon, 2017. "Calculating the excellence shift: How efficiently do institutions produce highly cited papers?," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1859-1864, September.
    18. Wohlrabe, Klaus & Bornmann, Lutz & de Moya Anegon, Felix, 2017. "Wie effizient sind Universitäten in Deutschland, deren Zukunftskonzepte im Rahmen der Exzellenzinitiative ausgezeichnet wurden? Ein empirischer Vergleich von Input- und Output-Daten zur Forschung [," MPRA Paper 76218, University Library of Munich, Germany.
    19. Mutz, Rüdiger & Bornmann, Lutz & Daniel, Hans-Dieter, 2017. "Are there any frontiers of research performance? Efficiency measurement of funded research projects with the Bayesian stochastic frontier analysis for count data," Journal of Informetrics, Elsevier, vol. 11(3), pages 613-628.
    20. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2018. "Research assessment by percentile-based double rank analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 315-329.

    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:119:y:2019:i:2:d:10.1007_s11192-019-03078-z. 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.