IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v10y2016i4p1143-1152.html
   My bibliography  Save this article

Towards field-adjusted production: Estimating research productivity from a zero-truncated distribution

Author

Listed:
  • Koski, Timo
  • Sandström, Erik
  • Sandström, Ulf

Abstract

Measures of research productivity (e.g. peer reviewed papers per researcher) is a fundamental part of bibliometric studies, but is often restricted by the properties of the data available. This paper addresses that fundamental issue and presents a detailed method for estimation of productivity (peer reviewed papers per researcher) based on data available in bibliographic databases (e.g. Web of Science and Scopus). The method can, for example, be used to estimate average productivity in different fields, and such field reference values can be used to produce field adjusted production values. Being able to produce such field adjusted production values could dramatically increase the relevance of bibliometric rankings and other bibliometric performance indicators. The results indicate that the estimations are reasonably stable given a sufficiently large data set.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:4:p:1143-1152
    DOI: 10.1016/j.joi.2016.09.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157716300888
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2016.09.002?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.
    2. Quentin L. Burrell, 2004. "Sample‐size dependence or time dependence of statistical measures in informetrics?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(2), pages 183-184, January.
    3. L. Egghe, 2005. "The power of power laws and an interpretation of Lotkaian informetric systems as self‐similar fractals," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(7), pages 669-675, May.
    4. Leydesdorff, Loet & Wagner, Caroline, 2009. "Macro-level indicators of the relations between research funding and research output," Journal of Informetrics, Elsevier, vol. 3(4), pages 353-362.
    5. Thomas Gurney & Edwin Horlings & Peter van den Besselaar, 2012. "Author disambiguation using multi-aspect similarity indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 435-449, May.
    6. Ulf Sandström & Erik Sandström, 2009. "The field factor: towards a metric for academic institutions," Research Evaluation, Oxford University Press, vol. 18(3), pages 243-250, September.
    7. Philippe Mongeon & Adèle Paul-Hus, 2016. "The journal coverage of Web of Science and Scopus: a comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 213-228, January.
    8. Caterina Dimaki & Evdokia Xekalaki, 1996. "Towards a unification of certain characterizations by conditional expectations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 48(1), pages 157-168, March.
    9. Telcs, A. & Glanzel, W. & Schubert, A., 1985. "Characterization and statistical test using truncated expectations for a class of skew distributions," Mathematical Social Sciences, Elsevier, vol. 10(2), pages 169-178, October.
    10. Waltman, Ludo & van Eck, Nees Jan, 2015. "Field-normalized citation impact indicators and the choice of an appropriate counting method," Journal of Informetrics, Elsevier, vol. 9(4), pages 872-894.
    11. José M. Soler, 2007. "Separating the articles of authors with the same name," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(2), pages 281-290, August.
    12. Hicks, Diana, 2012. "Performance-based university research funding systems," Research Policy, Elsevier, vol. 41(2), pages 251-261.
    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. Hamdi A. Al-Jamimi & Galal M. BinMakhashen & Lutz Bornmann, 2022. "Use of bibliometrics for research evaluation in emerging markets economies: a review and discussion of bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5879-5930, October.
    2. Gauffriau, Marianne, 2017. "A categorization of arguments for counting methods for publication and citation indicators," Journal of Informetrics, Elsevier, vol. 11(3), pages 672-684.
    3. Bornmann, Lutz & Williams, Richard, 2017. "Can the journal impact factor be used as a criterion for the selection of junior researchers? A large-scale empirical study based on ResearcherID data," Journal of Informetrics, Elsevier, vol. 11(3), pages 788-799.
    4. Koh Yamamoto & Takuo Yasunaga, 2022. "A percentile rank score of group productivity: an evaluation of publication productivity for researchers from various fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1737-1754, April.

    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. Benedetto Lepori & Aldo Geuna & Antonietta Mira, 2019. "Scientific output scales with resources. A comparison of US and European universities," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-18, October.
    2. Zaida Chinchilla-Rodríguez & Cassidy R Sugimoto & Vincent Larivière, 2019. "Follow the leader: On the relationship between leadership and scholarly impact in international collaborations," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-18, June.
    3. Stephen, Dimity & Stahlschmidt, Stephan, 2021. "Performance and structures of the German science system 2021," Studien zum deutschen Innovationssystem 5-2021, Expertenkommission Forschung und Innovation (EFI) - Commission of Experts for Research and Innovation, Berlin.
    4. 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.
    5. Omar Hernando Avila-Poveda, 2014. "Technical report: the trend of author compound names and its implications for authorship identity identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 833-846, October.
    6. van den Besselaar, Peter & Heyman, Ulf & Sandström, Ulf, 2017. "Perverse effects of output-based research funding? Butler’s Australian case revisited," Journal of Informetrics, Elsevier, vol. 11(3), pages 905-918.
    7. Gerhard Reichmann & Christian Schlögl, 2022. "On the possibilities of presenting the research performance of an institute over a long period of time: the case of the Institute of Information Science at the University of Graz in Austria," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3193-3223, June.
    8. Yuret, Tolga, 2017. "Do researchers pay attention to publication subsidies?," Journal of Informetrics, Elsevier, vol. 11(2), pages 423-434.
    9. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2021. "What do we know from the vast literature on efficiency and productivity in healthcare? A Systematic Review and Bibliometric Analysis," CEPA Working Papers Series WP092021, School of Economics, University of Queensland, Australia.
    10. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    11. Abramo, Giovanni & D'Angelo, Ciriaco Andrea & Di Costa, Flavia, 2022. "Revealing the scientific comparative advantage of nations: Common and distinctive features," Journal of Informetrics, Elsevier, vol. 16(1).
    12. Osmo Kivinen & Juha Hedman & Kalle Artukka, 2017. "Scientific publishing and global university rankings. How well are top publishing universities recognized?," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 679-695, July.
    13. Calabrese, Armando & Capece, Guendalina & Costa, Roberta & Di Pillo, Francesca & Giuffrida, Stefania, 2018. "A ‘power law’ based method to reduce size-related bias in indicators of knowledge performance: An application to university research assessment," Journal of Informetrics, Elsevier, vol. 12(4), pages 1263-1281.
    14. Maxim Kotsemir & Sergey Shashnov, 2017. "Measuring, analysis and visualization of research capacity of university at the level of departments and staff members," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1659-1689, September.
    15. Wolfgang G. Stock & Isabelle Dorsch & Gerhard Reichmann & Christian Schlögl, 2023. "Labor productivity, labor impact, and co-authorship of research institutions: publications and citations per full-time equivalents," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 363-377, January.
    16. Young-Hwan Lee, 2021. "Determinants of research productivity in Korean Universities: the role of research funding," The Journal of Technology Transfer, Springer, vol. 46(5), pages 1462-1486, October.
    17. R. D. Shelton, 2020. "Scientometric laws connecting publication counts to national research funding," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 181-206, April.
    18. Koh Yamamoto & Takuo Yasunaga, 2022. "A percentile rank score of group productivity: an evaluation of publication productivity for researchers from various fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1737-1754, April.
    19. Ritzen, Jo, 2020. "Public universities, in search of enhanced funding," MERIT Working Papers 2020-020, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    20. 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.

    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:eee:infome:v:10:y:2016:i:4:p:1143-1152. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    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.