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A note on empirical sample distribution of journal impact factors in major discipline groups

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  • Mishra, SK

Abstract

What type of statistical distribution do the Journal Impact Factors follow? In the past, researchers have hypothesized various types of statistical distributions underlying the generation mechanism of journal impact factors. These are: lognormal, normal, approximately normal, Weibull, negative exponential, combination of exponentials, Poisson, Generalized inverse Gaussian-Poisson, negative binomial, generalized Waring, gamma, etc. It is pertinent to note that the major characteristics of JIF data lay in the asymmetry and non-mesokurticity. The present study, frequently encounters Burr-XII, inverse Burr-III (Dagum), Johnson SU, and a few other distributions closely related to Burr distributions to best fit the JIF data in subject groups such as biology, chemistry, economics, engineering, physics, psychology and social sciences.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 20747.

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Date of creation: 14 Feb 2010
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Handle: RePEc:pra:mprapa:20747

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Keywords: Journal impact factor; JIF; theoretical probability distribution; Burr; Dagum; Generalized extreme value; generalized gamma; Inverse Gaussian; Johnson SU; Johnson SB; Kumaraswamy; Log-logistic; lognonmal; log-Pearson; Weibull; Generalized normal; Hypersecant; Beta; empirical distribution; sample;

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  1. Egghe, L., 2009. "Mathematical derivation of the impact factor distribution," Journal of Informetrics, Elsevier, Elsevier, vol. 3(4), pages 290-295.
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Cited by:
  1. Mishra, SK, 2010. "Temporal changes in the parameters of statistical distribution of journal impact factor," MPRA Paper, University Library of Munich, Germany 21263, University Library of Munich, Germany.
  2. Brzezinski, Michal, 2014. "Empirical modeling of the impact factor distribution," Journal of Informetrics, Elsevier, Elsevier, vol. 8(2), pages 362-368.

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