A note on empirical sample distribution of journal impact factors in major discipline groups
AbstractWhat 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 InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 20747.
Date of creation: 14 Feb 2010
Date of revision:
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;
Find related papers by JEL classification:
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-02-27 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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