Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient
Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient
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DOI：
10.1002/asi.10242
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发表时间：
2003-04-01
影响因子：
--
通讯作者：
Rousseau, R
中科院分区：
文献类型：
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作者：
Ahlgren, P;Jarneving, B;Rousseau, R
作者引证分析是由白色和格里菲斯于1981年提出的一种特殊的引证分析方法。这种技术用于分析特定科学领域的知识结构。1990年，麦凯恩发表了一份技术概述，该概述已被广泛采用为标准。在这里，McCain指出，皮尔逊相关系数（Pearson's r）经常被用作ACA中的相似性度量，并提出了使用它的一些优点。本文批评了在蚁群算法中使用皮尔逊r，并提出了两个自然的要求，应用在蚁群算法中的相似性度量应满足。结果表明，皮尔逊的r不满足这些要求。为了获得这两个要求的反例，使用了真实的和假设的数据。得出的结论是，皮尔逊的r可能不是一个最佳的选择，在ACA的相似性度量。尽管如此，还需要进一步的实证研究来证明，如果，在这种情况下，在何种程度上，在ACA，满足这些要求的相似性措施的使用将导致客观上更好的结果，在全面的研究。进一步讨论了不完全共点矩阵的有关问题。
Author cocitation analysis (ACA), a special type of cocitation analysis, was introduced by White and Griffith in 1981. This technique is used to analyze the intellectual structure of a given scientific field. In 1990, McCain published a technical overview that has been largely adopted as a standard. Here, McCain notes that Pearson's correlation coefficient (Pearson's r) is often used as a similarity measure in ACA and presents some advantages of its use. The present article criticizes the use of Pearson's r in ACA and sets forth two natural requirements that a similarity measure applied in ACA should satisfy. It is shown that Pearson's r does not satisfy these requirements. Real and hypothetical data are used in order to obtain counterexamples to both requirements. It is concluded that Pearson's r is probably not an optimal choice of a similarity measure in ACA. Still, further empirical research is needed to show if, and in that case to what extent, the use of similarity measures in ACA that fulfill these requirements would lead to objectively better results in full-scale studies. Further, problems related to incomplete cocitation matrices are discussed.