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Int. braz. j. urol ; 41(6): 1058-1066, Nov.-Dec. 2015. tab, graf
Article in English | LILACS | ID: lil-769766

ABSTRACT

Purpose: The Journal Impact Factor (JIF) is an index used to compare a journal's quality among academic journals and it is commonly used as a proxy for journal quality. We sought to examine the JIF in order to elucidate the main predictors of the index while generating awareness among scientific community regarding need to modify the index calculation in the attempt to turn it more accurate. Materials and Methods: Under the Urology and Nephrology category in the Journal Citations Report Website, the top 17 Journals by JIF in 2011 were chosen for the study. All manuscripts’ abstracts published from 2009-2010 were reviewed; each article was categorized based on its research design (Retrospective, Review, etc). T and correlation tests were performed for categorical and continuous variables respectively. The JIF was the dependent variable. All variables were then included in a multivariate model. Results: 23,012 articles from seventeen journals were evaluated with a median of 1,048 (range=78-6,342) articles per journal. Journals with a society affiliation were associated with a higher JIF (p=0.05). Self-citations (rho=0.57, p=0.02), citations for citable articles (rho=0.73, p=0.001), citations to non-citable articles (rho=0.65, p=0.0046), and retrospective studies (rho=-0.51, p=0.03) showed a strong correlation. Slight modifications to include the non-citable articles in the denominator yield drastic changes in the JIF and the ranking of the journals. Conclusion: The JIF appears to be closely associated with the number of citable articles published. A change in the formula for calculating JIF to include all types of published articles in the denominator would result in a more accurate representation.


Subject(s)
Journal Impact Factor , Nephrology/statistics & numerical data , Periodicals as Topic/statistics & numerical data , Urology/statistics & numerical data , Databases, Bibliographic , Linear Models , Publishing/statistics & numerical data , Research Design , Statistics, Nonparametric
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