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Temporal changes in the parameters of statistical distribution of journal impact factor

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  • Mishra, SK

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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 21263.

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Date of creation: 10 Mar 2010
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Handle: RePEc:pra:mprapa:21263

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Keywords: Journal Impact Factor; Johnson SU Distribution; Mathew effect; over-the-samples stability; bootstrapping; Pearson distribution type IV; re-sampling; skewness; kurtosis; temporal variations;

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  1. 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.
  2. Mishra, SK, 2010. "A note on empirical sample distribution of journal impact factors in major discipline groups," MPRA Paper 20747, University Library of Munich, Germany.
  3. Mishra, SK, 2009. "Does the Journal Impact Factor help make a Good Indicator of Academic Performance?," MPRA Paper 17712, University Library of Munich, Germany.
  4. Pandu Tadikamalla, 1980. "On simulating non-normal distributions," Psychometrika, Springer, vol. 45(2), pages 273-279, June.
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