IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v125y2020i2d10.1007_s11192-020-03654-8.html
   My bibliography  Save this article

A new algorithm for zero-modified models applied to citation counts

Author

Listed:
  • Marzieh Shahmandi

    (University of Wolverhampton)

  • Paul Wilson

    (University of Wolverhampton)

  • Mike Thelwall

    (University of Wolverhampton)

Abstract

Finding statistical models for citation count data is important for those seeking to understand the citing process or when using regression to identify factors that associate with citation rates. As sets of citation counts often include more or less zeros (uncited articles) than would be expected under the base distribution, it is essential to deal appropriately with them. This article proposes a new algorithm to fit zero-modified versions of discretised log-normal, hooked power-law and Weibull models to citation count data from 23 different Scopus categories from 2012. The new algorithm allows the standard errors of all parameter estimates to be calculated, and hence also confidence intervals and p-values. This algorithm can also estimate negative zero-modification parameters corresponding to zero-deflation (fewer uncited articles than expected). The results find no universal best model for the 23 categories. A given dataset may be zero-inflated relative to one model, but zero-deflated relative to another. We suggest circumstances in which one of the models under consideration may be the best fitting model.

Suggested Citation

  • Marzieh Shahmandi & Paul Wilson & Mike Thelwall, 2020. "A new algorithm for zero-modified models applied to citation counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 993-1010, November.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:2:d:10.1007_s11192-020-03654-8
    DOI: 10.1007/s11192-020-03654-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-020-03654-8
    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/s11192-020-03654-8?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. Michal Brzezinski, 2015. "Power laws in citation distributions: evidence from Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 213-228, April.
    2. Wilson, Paul, 2015. "The misuse of the Vuong test for non-nested models to test for zero-inflation," Economics Letters, Elsevier, vol. 127(C), pages 51-53.
    3. Dietz, Ekkehart & Bohning, Dankmar, 2000. "On estimation of the Poisson parameter in zero-modified Poisson models," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 441-459, October.
    4. Thelwall, Mike, 2016. "Are there too many uncited articles? Zero inflated variants of the discretised lognormal and hooked power law distributions," Journal of Informetrics, Elsevier, vol. 10(2), pages 622-633.
    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. Katiane S. Conceição & Marinho G. Andrade & Francisco Louzada & Nalini Ravishanker, 2022. "Characterizations and generalizations of the negative binomial distribution," Computational Statistics, Springer, vol. 37(3), pages 1255-1286, July.
    2. Katchanov, Yurij L. & Markova, Yulia V. & Shmatko, Natalia A., 2023. "Uncited papers in the structure of scientific communication," Journal of Informetrics, Elsevier, vol. 17(2).

    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. Guillermo Armando Ronda-Pupo & J. Sylvan Katz, 2018. "The power law relationship between citation impact and multi-authorship patterns in articles in Information Science & Library Science journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 919-932, March.
    2. Guillermo Armando Ronda-Pupo & J. Sylvan Katz, 2017. "The scaling relationship between degree centrality of countries and their citation-based performance on Management Information Systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1285-1299, September.
    3. Mrowinski, Maciej J. & Gagolewski, Marek & Siudem, Grzegorz, 2022. "Accidentality in journal citation patterns," Journal of Informetrics, Elsevier, vol. 16(4).
    4. Banshal, Sumit Kumar & Gupta, Solanki & Lathabai, Hiran H & Singh, Vivek Kumar, 2022. "Power Laws in altmetrics: An empirical analysis," Journal of Informetrics, Elsevier, vol. 16(3).
    5. Bruno Michel Roman Pais Seles & Janaina Mascarenhas & Ana Beatriz Lopes de Sousa Jabbour & Adriana Hoffman Trevisan, 2022. "Smoothing the circular economy transition: The role of resources and capabilities enablers," Business Strategy and the Environment, Wiley Blackwell, vol. 31(4), pages 1814-1837, May.
    6. Monika Stachowiak-Kudła & Janusz Kudła, 2023. "Measuring the prestige of administrative courts," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3637-3662, August.
    7. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    8. Virginia Milone & Antonio Fusco & Angelamaria De Feo & Marco Tatullo, 2024. "Clinical Impact of “Real World Data” and Blockchain on Public Health: A Scoping Review," IJERPH, MDPI, vol. 21(1), pages 1-14, January.
    9. Georgios Stoupas & Antonis Sidiropoulos & Antonia Gogoglou & Dimitrios Katsaros & Yannis Manolopoulos, 2018. "Rainbow ranking: an adaptable, multidimensional ranking method for publication sets," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 147-160, July.
    10. Ehsan Bahrami Samani & Elham Tabrizi, 2023. "Joint Linear Modeling of Mixed Data and Its Application to Email Analysis," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 175-209, May.
    11. Craig W. Carpenter & Anders Van Sandt & Rebekka Dudensing & Scott Loveridge, 2022. "Profit Pools and Determinants of Potential County-Level Manufacturing Growth," International Regional Science Review, , vol. 45(2), pages 188-224, March.
    12. Mike Thelwall, 2017. "Are Mendeley reader counts useful impact indicators in all fields?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1721-1731, December.
    13. David Dale & Andrei Sirchenko, 2021. "Estimation of nested and zero-inflated ordered probit models," Stata Journal, StataCorp LP, vol. 21(1), pages 3-38, March.
    14. Federica Caboni, 2021. "The Use of Digital Technology to Reshape the Retail Store," International Journal of Business and Management, Canadian Center of Science and Education, vol. 15(1), pages 149-149, July.
    15. Paula Camargo Fiorini & Charbel Jose Chiappetta Jabbour & Ana Beatriz Lopes de Sousa Jabbour & Gary Ramsden, 2022. "The human side of humanitarian supply chains: a research agenda and systematization framework," Annals of Operations Research, Springer, vol. 319(1), pages 911-936, December.
    16. Rafat Mahmood & Michael Jetter, 2020. "Communications Technology and Terrorism," Journal of Conflict Resolution, Peace Science Society (International), vol. 64(1), pages 127-166, January.
    17. Unnikrishnan Nair, N. & Vineshkumar, B., 2022. "Modelling informetric data using quantile functions," Journal of Informetrics, Elsevier, vol. 16(2).
    18. Zewen Hu & Yishan Wu & Jianjun Sun, 2018. "A quantitative analysis of determinants of non-citation using a panel data model," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 843-861, August.
    19. Wesley Bertoli & Katiane S. Conceição & Marinho G. Andrade & Francisco Louzada, 2018. "On the zero-modified Poisson–Shanker regression model and its application to fetal deaths notification data," Computational Statistics, Springer, vol. 33(2), pages 807-836, June.
    20. Lopes de Sousa Jabbour, Ana Beatriz & Ndubisi, Nelson Oly & Roman Pais Seles, Bruno Michel, 2020. "Sustainable development in Asian manufacturing SMEs: Progress and directions," International Journal of Production Economics, Elsevier, vol. 225(C).

    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:125:y:2020:i:2:d:10.1007_s11192-020-03654-8. 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.