IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/20919.html
   My bibliography  Save this paper

Empirical probability distribution of journal impact factor and over-the-samples stability in its estimated parameters

Author

Abstract

The data on JIFs provided by Thomson Scientific can only be considered as a sample since they do not cover the entire universe of those documents that cite an intellectual output (paper, article, etc) or are cited by others. Then, questions arise if the empirical distribution (best fit to the JIF data for any particular year) really represents the true or universal distribution, are its estimated parameters stable over the samples and do they have some scientific interpretation? It may be noted that if the estimated parameters do not exhibit stability over the samples (while the sample size is large enough), they cannot be scientifically meaningful, since science is necessarily related with a considerable degree of regularity and predictability. Stability of parameters is also a precondition to other statistical properties such as consistency. If the estimated parameters lack in stability and scientific meaning, then the empirical distribution, howsoever fit to data, has little significance. This study finds that although Burr-4p, Dagum-4p and Johnson SU distributions fit extremely well to the sub-samples, the parameters of the first two distributions do not have stability over the subsamples. The Johnson SU parameters have this property.

Suggested Citation

  • Mishra, SK, 2010. "Empirical probability distribution of journal impact factor and over-the-samples stability in its estimated parameters," MPRA Paper 20919, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:20919
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/20919/1/MPRA_paper_20919.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael J Stringer & Marta Sales-Pardo & Luís A Nunes Amaral, 2008. "Effectiveness of Journal Ranking Schemes as a Tool for Locating Information," PLOS ONE, Public Library of Science, vol. 3(2), pages 1-8, February.
    2. Richard S.J. Tol, 2009. "The Matthew effect defined and tested for the 100 most prolific economists," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(2), pages 420-426, February.
    3. Singh, S K & Maddala, G S, 1976. "A Function for Size Distribution of Incomes," Econometrica, Econometric Society, vol. 44(5), pages 963-970, September.
    4. Egghe, L., 2009. "Mathematical derivation of the impact factor distribution," Journal of Informetrics, Elsevier, vol. 3(4), pages 290-295.
    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. Mishra, SK, 2010. "Temporal changes in the parameters of statistical distribution of journal impact factor," MPRA Paper 21263, University Library of Munich, Germany.

    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. B Ian Hutchins & Xin Yuan & James M Anderson & George M Santangelo, 2016. "Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level," PLOS Biology, Public Library of Science, vol. 14(9), pages 1-25, September.
    2. Brzezinski, Michal, 2014. "Empirical modeling of the impact factor distribution," Journal of Informetrics, Elsevier, vol. 8(2), pages 362-368.
    3. Dickens, Richard & Machin, Stephen & Manning, Alan, 1998. "Estimating the effect of minimum wages on employment from the distribution of wages: A critical view," Labour Economics, Elsevier, vol. 5(2), pages 109-134, June.
    4. Sarabia, José María, 2008. "A general definition of the Leimkuhler curve," Journal of Informetrics, Elsevier, vol. 2(2), pages 156-163.
    5. Fabio Clementi & Mauro Gallegati & Giorgio Kaniadakis, 2010. "A model of personal income distribution with application to Italian data," Empirical Economics, Springer, vol. 39(2), pages 559-591, October.
    6. Fabio Fiorillo & Agnese Sacchi, 2012. "The Political Economy of the Standard Level of Services: The Role of Income Distribution," CESifo Working Paper Series 3696, CESifo.
    7. van den Berg, Gerard J., 2007. "On the uniqueness of optimal prices set by monopolistic sellers," Journal of Econometrics, Elsevier, vol. 141(2), pages 482-491, December.
    8. Sigurd Galaasen & Rustam Jamilov & Hélène Rey & Ragnar Juelsrud, 2020. "Granular credit risk," Working Paper 2020/15, Norges Bank.
    9. Perc, Matjaž, 2010. "Zipf’s law and log-normal distributions in measures of scientific output across fields and institutions: 40 years of Slovenia’s research as an example," Journal of Informetrics, Elsevier, vol. 4(3), pages 358-364.
    10. Sherrick, Bruce J. & Forster, D. Lynn & Irwin, Scott H., 1990. "The Calibration Of Expected Soybean Price Distributions: An Option Based Approach," 1990 Annual meeting, August 5-8, Vancouver, Canada 270919, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Masato Okamoto, 2014. "Interpolating the Lorenz Curve: Methods to Preserve Shape and Remain Consistent with the Concentration Curves for Components," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(2), pages 349-384, June.
    12. Paul Gregg & Lindsey Macmillan & Claudia Vittori, 2017. "Moving Towards Estimating Sons' Lifetime Intergenerational Economic Mobility in the UK," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(1), pages 79-100, February.
    13. Cowell, Frank A. & Flachaire, Emmanuel, 2007. "Income distribution and inequality measurement: The problem of extreme values," Journal of Econometrics, Elsevier, vol. 141(2), pages 1044-1072, December.
    14. Dorothée Boccanfuso & Bernard Decaluwé & Luc Savard, 2008. "Poverty, income distribution and CGE micro-simulation modeling: Does the functional form of distribution matter?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 6(2), pages 149-184, June.
    15. Ekaterina Selezneva & Philippe Van Kerm, 2016. "A distribution-sensitive examination of the gender wage gap in Germany," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 14(1), pages 21-40, March.
    16. Keighley, Tim & Longden, Thomas & Mathew, Supriya & Trück, Stefan, 2014. "Quantifying Catastrophic and Climate Impacted Hazards Based on Local Expert Opinions," Climate Change and Sustainable Development 189171, Fondazione Eni Enrico Mattei (FEEM).
    17. Vladimir Hlasny & Paolo Verme, 2018. "Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data," Econometrics, MDPI, vol. 6(2), pages 1-21, June.
    18. Bárbara S. Lancho-Barrantes & Vicente P. Guerrero-Bote & Félix Moya-Anegón, 2010. "The iceberg hypothesis revisited," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(2), pages 443-461, November.
    19. Hakkio, Craig S. & Rush, Mark & Schmidt, Timothy J., 1996. "The marginal income tax rate schedule from 1930 to 1990," Journal of Monetary Economics, Elsevier, vol. 38(1), pages 117-138, August.
    20. David I Stern, 2014. "High-Ranked Social Science Journal Articles Can Be Identified from Early Citation Information," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-11, November.

    More about this item

    Keywords

    Journal Impact Factor; JIF 2008; Burr-XII; Dagum; Johnson SU; empirical probability distribution; over-the-samples stability in parameters; skewness; kurtosis;
    All these keywords.

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

    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:pra:mprapa:20919. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.