A multivariate model for evaluating emergency medicine journals

Antonio Fernandez-Cano () and Inés M. Fernández-Guerrero ()
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Antonio Fernandez-Cano: University of Granada
Inés M. Fernández-Guerrero: General University Hospital “Virgen de las Nieves”

Scientometrics, 2017, vol. 110, issue 2, No 22, 1003 pages

Abstract: Abstract This paper proposes a multivariate model for the evaluation of international emergency medicine journals using the most widely used, efficient and representative productivity and citation indicators. This is a descriptive observational evaluation study based on a sample of 24 journals included in the emergency medicine category of the Journal Citation Reports 2015. The sample is evaluated based on seven evaluation indicators: Journal Citation Reports impact factor, three H-indices (Web of Science, Scimago and Google Scholar), the Scimago Journal Rank and two altmetric scores (3-month and any time). The bivariate correlations between the diverse distributions of evaluation indicators and a multivariate metric reliability index are calculated. The factorial structure of the indicators is explored and clusters of journals are defined. A factor score is assigned to each journal. The correlations between the seven evaluation indicators are high and statistically significant. The metric reliability of the multivariate analysis calculated using Cronbach’s alpha is .97. A general factor explains the 84.64% variance of the factor space of the seven evaluation indicators, representing the ‘quality of EM journals’ construct. The most well-represented indicator, the one with the highest communality, is H_Index_WoS (h = .922), which is also the indicator with the greatest weight in the general factor (a = .96). The journals can be classified in four clusters according to their quality. A factor score is generated for each journal that could be used as a multivariate meta-index to evaluate journal quality and to define a ranking of journals.

Keywords: Scientific journals; Emergency medicine; Multivariate evaluation; Factor analysis; Cluster analysis; Factor scores (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-016-2197-6

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