A principal component analysis of 39 scientific impact measures.
A principal component analysis of 39 scientific impact measures.
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DOI：
10.1371/journal.pone.0006022
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发表时间：
2009-06-29
期刊：
影响因子：
3.7
通讯作者：
Chute R
中科院分区：
文献类型：
--
作者：
Bollen J;Van de Sompel H;Hagberg A;Chute R
传统上，科学出版物的影响是用引用数来表示的。然而，在过去的十年里，科学活动已经转移到了网上。为了更好地捕捉数字时代的科学影响，在社交网络分析和使用日志数据的基础上，提出了各种新的影响措施。在这里，我们将研究这些新措施如何相互关联，以及它们如何准确和完整地表达科学影响。我们对基于引用和使用日志数据计算的39项现有和拟议的学术影响指标所产生的排名进行了主成分分析。我们的研究结果表明，科学影响的概念是一个多维的概念，不能用任何单一的指标来充分衡量，尽管一些指标比其他指标更合适。常用的引文影响因子并不是这个结构的核心，而是它的外围，因此应该谨慎使用。
The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.