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

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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
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    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. Singh, S K & Maddala, G S, 1976. "A Function for Size Distribution of Incomes," Econometrica, Econometric Society, vol. 44(5), pages 963-970, September.
    3. 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.
    4. Egghe, L., 2009. "Mathematical derivation of the impact factor distribution," Journal of Informetrics, Elsevier, vol. 3(4), pages 290-295.
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    1. Mishra, SK, 2010. "Temporal changes in the parameters of statistical distribution of journal impact factor," MPRA Paper 21263, University Library of Munich, Germany.

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    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

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