Mishra, SK (2010): Empirical probability distribution of journal impact factor and overthesamples 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 Burr4p, Dagum4p and Johnson SU distributions fit extremely well to the subsamples, the parameters of the first two distributions do not have stability over the subsamples. The Johnson SU parameters have this property.
Item Type:  MPRA Paper 

Original Title:  Empirical probability distribution of journal impact factor and overthesamples stability in its estimated parameters 
Language:  English 
Keywords:  Journal Impact Factor; JIF 2008; BurrXII; Dagum; Johnson SU; empirical probability distribution; overthesamples stability in parameters; skewness; kurtosis 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C16  Specific Distributions C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C46  Specific Distributions; Specific Statistics 
Item ID:  20919 
Depositing User:  Sudhanshu Kumar Mishra 
Date Deposited:  25. Feb 2010 18:35 
Last Modified:  16. Feb 2013 18:44 
References:  Avramescu, A. (1979) “Actuality and Obsolescence of Scientific Literature”, Journal of the American Society for Information Science, 30(5): 296303. Bensman, S. J. (2008) “Distributional Differences of the Impact Factor in the Sciences Versus the Social Sciences: An Analysis of the Probabilistic Structure of the 2005 Journal Citation Reports”, Journal of the American Society for Information Science and Technology, 59(9): 1366–1382. Brookes, B. C. (1970) “The Growth, Utility, and Obsolescence of Scientific Periodical Literature”, Journal of Documentation, 26(4): 283294. Brown, P. (1980) “The Halflife of Chemical Literature”, Journal of the American Society for Information Science, 31(1): 6163. Burrell, Q. and Fenton, M. R. (1993) “Yes, the GIGP Really Does Work – and is Workable”, Journal of the American Society for Information Science, 44(2): 6169. Egghe, L. (2009) “Mathematical Derivation of the Impact Factor Distribution”, Journal of Informetrics, 3(4): 290295. Egghe, L. and Rao, I. K. (1992) “Citation Age of Data and the Obsolescence Function: Fits and Explanations”, Information Processing and Management, 28(2): 201217. Glanzel, W. (2009) “The Multidimensionality of Journal Impact”, Scientometrics, 78(2): 355374. Hurt, C. D. and Budd, J. M. (1992) “Modeling the Literature of Superstring Theory: A Case of Fast Literature”, Scientometrics, 24(3): 471480. Irwin, J. O. (1975) “The Generalized Waring Distribution”. Part I, Journal of the Royal Statistical Society. Series A (General), 138: 18–31. Matricciani, E. (1991) “The Probability Distribution of the Age of References in Engineering Papers”, IEEE Transactions on Professional Communication, 34(1): 712. Mishra, S. K. (2010) “A Note on Empirical Sample Distribution of Journal Impact Factors in Major Discipline Groups”, available at SSRN: http://ssrn.com/abstract=1552723 Panaretos, J. and Xekalaki, E. (1986) “The Stuttering Generalized Waring Distribution”, Statistics & Probability Letters, 4(1986) 313318. Rousseau, R. and WestVlaanderen, K. I. H. (1993) “A Note on Maximum Impact Factors”, Available at http://www.caisacsi.ca/proceedings/1993/Rousseau_1993.pdf Sichel, H.S. (1985) “A Bibliometric Distribution which Really Works”, Journal of the American Society for Information Science, 36: 314321. Sahoo, B. B. and Rao, I. K. R. (2006) “A Distribution of Impact Factors of Journals in the Area of Software: An Empirical Study”, Information Processing & Management, 42(6): 14651470. Singh, S. K., and Maddala, G. S. (1976) “A Function for Size Distribution of Incomes”, Econometrica, 44(5): 963970. Stringer, M. J., SalesPardo, M. and Amaral, L. A. N. (2008) “Effectiveness of Journal Ranking Schemes as a Tool for Locating Information”, PLoS ONE 3(2): e1683. doi:10.1371/ journal.pone.0001683: 18. Tol, R. S. J. (2009). “The Matthew effect defined and tested for the 100 most prolific economists”, Journal of the American Society for Information Science and Technology, 60(2): 420426. Wikipedia (2010) “Impact Factor”, available at http://en.wikipedia.org/wiki/Impact_factor 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/20919 