Mishra, SK (2010): Temporal changes in the parameters of statistical distribution of journal impact factor.
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Abstract
Statistical distribution of Journal Impact Factor (JIF) is characteristically asymmetric and non-mesokurtic. Even the distribution of log10(JIF) exhibits conspicuous skewness and non-mesokurticity. In this paper we estimate the parameters of Johnson SU distribution fitting to the log10(JIF) data for 8 years, 2001 through 2008, and study the temporal variations in those estimated parameters. We also study ‘over-the-samples stability’ in the estimated parameters for each year by the method of re-sampling close to bootstrapping. It has been found that log10(JIF) is Pearson-IV distributed. Johnson SU distribution fits very well to the data and yields parameters stable over the samples. We conclude that Johnson SU distribution is the best choice to fit to the log10(JIF) data. We have also found that over the years the log10(JIF) distribution is becoming more skewed and leptokurtic, possibly suggesting the Mathew effect in operation, which means that more cited journals are cited ever more over time.
Item Type: | MPRA Paper |
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Original Title: | Temporal changes in the parameters of statistical distribution of journal impact factor |
Language: | English |
Keywords: | Journal Impact Factor; Johnson SU Distribution; Mathew effect; over-the-samples stability; bootstrapping; Pearson distribution type IV; re-sampling; skewness; kurtosis; temporal variations |
Subjects: | C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C46 - Specific Distributions ; Specific Statistics C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General |
Item ID: | 21263 |
Depositing User: | Sudhanshu Kumar Mishra |
Date Deposited: | 11 Mar 2010 01:38 |
Last Modified: | 03 Oct 2019 11:36 |
References: | 1. Draper, J. (1952) “Properties of Distributions Resulting from Certain Simple Transformations of the Normal Distribution”, Biometrika, 39(3-4): 290-301. 2. George, F. (2007) Johnson's System of Distributions and Microarray Data Analysis, Doctoral Dissertation, Department of Mathematics, College of Arts and Sciences, University of South Florida, available at http://kong.lib.usf.edu:1801/view/action/singleViewer.do?dvs=1268014385536~52&locale=en_US&search_terms=000031367&application=DIGITOOL-3&frameId=1&usePid1=true&usePid2=true 3. Gupta, S.C. and Kapoor, V.K. (1982) Fundamentals of mathematical statistics [8th Edition], Sultan Chand & Sons, New Delhi. 4. Johnson, N.L.(1949) “Systems of Frequency Curves Generated by Methods of Translation”, Biometrika 36(1-2): 149-176. 5. Mishra, S. K. (2009) “Does the Journal Impact Factor Help Make a Good Indicator of Academic Performance?”, available at SSRN: http://ssrn.com/abstract=1485868 6. Mishra, S. K. (2010a) “A Note on Empirical Sample Distribution of Journal Impact Factors in Major Discipline Groups”, available at SSRN: http://ssrn.com/abstract=1552723 7. Mishra, S. K. (2010b) “Empirical Probability Distribution of Journal Impact Factor and Over-the-Samples Stability in its Estimated Parameters”, available at SSRN: http://ssrn.com/abstract=1556281 8. Slifker, J. and Shapiro, S. (1980) “The Johnson System: selection and parameter estimation”, Technometrics, 22(2): 239-247. 9. Tadikamalla, P. R. (1980) “On Simulating Non-normal Distributions”, Psychometrica, 45(2): 273-279. 10. 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): 420-426. 11. Wheeler, R. (1980) “Quantile Estimators of Johnson curve Parameters”, Biometrika, 67(3): 725-728. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/21263 |