IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v109y2016i3d10.1007_s11192-016-2156-2.html
   My bibliography  Save this article

Does the average JIF percentile make a difference?

Author

Listed:
  • Liping Yu

    (Yunan University of finance and economics)

  • Houqiang Yu

    (Wuhan University)

Abstract

Average journal impact factor (JIF) percentile is a novel bibliometric indicator introduced by Thomson Reuters. It’s of great significance to study the characteristics of its data distribution and relationship with other bibliometric indicators, in order to assess its usefulness as a new bibliometric indicator. The research began by analyzing the meaning of average JIF percentile, and compared its statistical difference with impact factor. Based upon factor analysis, the paper used multivariate regression and quantile regression to study the relationship between average JIF percentile and other bibliometric indicators. Results showed that average JIF percentile had changed the statistical characteristic of impact factor, e.g. improved the relative value of impact factor, having smaller variation coefficient and distribution closer to normal distribution. Because it’s non-parametric transformation, it cannot be used to measure the relative gap between journals; Average JIF percentile had the highest regression coefficient with journal impact, followed by timeliness and lastly the citable items; The lower the average JIF percentile, the higher the elastic coefficient of journal impact; When average JIF percentile was extremely high or extremely low, citable items were not correlated with the average JIF percentile at all; When average JIF percentile was low, elastic coefficient of timeliness was even higher; Average JIF percentile was not a proper indicator for multivariate journal evaluation; Average JIF percentile had both the advantages and disadvantages of impact factor, and thus had the same limitation in applying as the impact factor.

Suggested Citation

  • Liping Yu & Houqiang Yu, 2016. "Does the average JIF percentile make a difference?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1979-1987, December.
    • Handle: RePEc:spr:scient:v:109:y:2016:i:3:d:10.1007_s11192-016-2156-2
    DOI: 10.1007/s11192-016-2156-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-016-2156-2
    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/s11192-016-2156-2?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. Bornmann, Lutz & Leydesdorff, Loet & Mutz, Rüdiger, 2013. "The use of percentiles and percentile rank classes in the analysis of bibliometric data: Opportunities and limits," Journal of Informetrics, Elsevier, vol. 7(1), pages 158-165.
    2. Loet Leydesdorff, 2012. "Alternatives to the journal impact factor: I3 and the top-10% (or top-25%?) of the most-highly cited papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(2), pages 355-365, August.
    3. Loet Leydesdorff & Lutz Bornmann, 2011. "Integrated impact indicators compared with impact factors: An alternative research design with policy implications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(11), pages 2133-2146, November.
    4. Lutz Bornmann, 2013. "The problem of percentile rank scores used with small reference sets," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(3), pages 650-650, March.
    5. Ronald Rousseau, 2012. "Basic properties of both percentile rank scores and the I3 indicator," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(2), pages 416-420, February.
    6. Campanario, Juan Miguel, 2014. "Analysis of the distribution of cited journals according to their positions in the h-core of citing journal listed in Journal Citation Reports," Journal of Informetrics, Elsevier, vol. 8(3), pages 534-545.
    7. Ronald Rousseau, 2012. "Basic properties of both percentile rank scores and the I3 indicator," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(2), pages 416-420, February.
    8. Zhou, Ping & Zhong, Yongfeng, 2012. "The citation-based indicator and combined impact indicator—New options for measuring impact," Journal of Informetrics, Elsevier, vol. 6(4), pages 631-638.
    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. Dzieżyc, Maciej & Kazienko, Przemysław, 2022. "Effectiveness of research grants funded by European Research Council and Polish National Science Centre," Journal of Informetrics, Elsevier, vol. 16(1).
    2. Sigifredo Laengle & Nikunja Mohan Modak & Jose M. Merigo & Gustavo Zurita, 2018. "Twenty-Five Years of Group Decision and Negotiation: A Bibliometric Overview," Group Decision and Negotiation, Springer, vol. 27(4), pages 505-542, August.
    3. Istvan-Szilard Szilagyi & Gregor A. Schittek & Christoph Klivinyi & Holger Simonis & Torsten Ulrich & Helmar Bornemann-Cimenti, 2022. "Citation of retracted research: a case-controlled, ten-year follow-up scientometric analysis of Scott S. Reuben’s malpractice," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2611-2620, May.
    4. Pilar Valderrama & Manuel Escabias & Evaristo Jiménez-Contreras & Mariano J. Valderrama & Pilar Baca, 2018. "A mixed longitudinal and cross-sectional model to forecast the journal impact factor in the field of Dentistry," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1203-1212, August.
    5. Boris Forthmann & Mark Leveling & Yixiao Dong & Denis Dumas, 2020. "Investigating the quantity–quality relationship in scientific creativity: an empirical examination of expected residual variance and the tilted funnel hypothesis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2497-2518, September.

    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. Bornmann, Lutz & Leydesdorff, Loet & Wang, Jian, 2013. "Which percentile-based approach should be preferred for calculating normalized citation impact values? An empirical comparison of five approaches including a newly developed citation-rank approach (P1," Journal of Informetrics, Elsevier, vol. 7(4), pages 933-944.
    2. 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.
    3. Lutz Bornmann & Alexander Tekles & Loet Leydesdorff, 2019. "How well does I3 perform for impact measurement compared to other bibliometric indicators? The convergent validity of several (field-normalized) indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 1187-1205, May.
    4. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2018. "Research assessment by percentile-based double rank analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 315-329.
    5. Schreiber, Michael, 2014. "How to improve the outcome of performance evaluations in terms of percentiles for citation frequencies of my papers," Journal of Informetrics, Elsevier, vol. 8(4), pages 873-879.
    6. Ludo Waltman & Michael Schreiber, 2013. "On the calculation of percentile-based bibliometric indicators," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 372-379, February.
    7. Mingers, John & Yang, Liying, 2017. "Evaluating journal quality: A review of journal citation indicators and ranking in business and management," European Journal of Operational Research, Elsevier, vol. 257(1), pages 323-337.
    8. Lutz Bornmann & Werner Marx & Andreas Barth, 2013. "The Normalization of Citation Counts Based on Classification Systems," Publications, MDPI, vol. 1(2), pages 1-9, August.
    9. Mingers, John & Leydesdorff, Loet, 2015. "A review of theory and practice in scientometrics," European Journal of Operational Research, Elsevier, vol. 246(1), pages 1-19.
    10. Schreiber, Michael, 2014. "Is the new citation-rank approach P100′ in bibliometrics really new?," Journal of Informetrics, Elsevier, vol. 8(4), pages 997-1004.
    11. Michael Schreiber, 2013. "How much do different ways of calculating percentiles influence the derived performance indicators? A case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 821-829, December.
    12. Loet Leydesdorff, 2013. "An evaluation of impacts in “Nanoscience & nanotechnology”: steps towards standards for citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 35-55, January.
    13. Bornmann, Lutz, 2013. "The problem of citation impact assessments for recent publication years in institutional evaluations," Journal of Informetrics, Elsevier, vol. 7(3), pages 722-729.
    14. Vinkler, Péter, 2014. "The use of the Percentage Rank Position index for comparative evaluation of journals," Journal of Informetrics, Elsevier, vol. 8(2), pages 340-348.
    15. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    16. Ping Zhou & Yongfeng Zhong & Meigen Yu, 2013. "A bibliometric investigation on China–UK collaboration in food and agriculture," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 267-285, November.
    17. David A. Pendlebury & Jonathan Adams, 2012. "Comments on a critique of the Thomson Reuters journal impact factor," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(2), pages 395-401, August.
    18. Zhesi Shen & Liying Yang & Zengru Di & Jinshan Wu, 2019. "Large enough sample size to rank two groups of data reliably according to their means," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 653-671, February.
    19. Péter Vinkler, 2017. "The size and impact of the elite set of publications in scientometric assessments," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 163-177, January.
    20. Zhou, Ping & Zhong, Yongfeng, 2012. "The citation-based indicator and combined impact indicator—New options for measuring impact," Journal of Informetrics, Elsevier, vol. 6(4), pages 631-638.

    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:109:y:2016:i:3:d:10.1007_s11192-016-2156-2. 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.