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Mean Algorithms for Composite Journal Citation Analysis Based on Multi-source Data
Received date: 2015-08-03
Revised date: 2015-09-13
Online published: 2015-10-05
[Purpose/significance] As three single-source databases, Scopus, Web of Science and Google Scholar all have limits in informetric researches and applications. The composite citation analysis method, which utilizes multi-source data, can provide a supplemental method. [Method/process] Based on the journal's total citations and the cases of international LIS journals, this paper attempts to compare the mean algorithms of composite citation analysis, including arithmetic mean, geometric mean and harmonic mean. [Result/conclusion] The results show that, though arithmetic mean is different from geometric mean or harmonic mean, there is a strong correlation between them. The cumulative arithmetic mean, cumulative geometric mean and harmonic mean do not follow Brad Ford's law, but with the same number of journals in three sub-zone, the cumulative value satisfies the model of "n2:n:1". The difference between journal rankings in single-source database and multi-source data appears as the phenomenon of "balance in ends, difference in mid". The journal ranking of arithmetic mean is closer to GS, while geometric mean is closer to Scopus and harmonic mean is closer to WoS.
Key words： Scopus; WoS; Google Scholar; Multi-source data; Composite citation analysis
Su Linwei , Yu Shuang , Xu Xin , Zhao Xing . Mean Algorithms for Composite Journal Citation Analysis Based on Multi-source Data[J]. Library and Information Service, 2015 , 59(19) : 100 -107 . DOI: 10.13266/j.issn.0252-3116.2015.19.013
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