巨无霸和特征因子分数：不要让相关系数愚弄你
Big Macs and Eigenfactor Scores: Don't Let Correlation Coefficients Fool You
摘要：
特征因子排名提供了一种基于类似于Google的PageRank算法的迭代排名程序来评估学术期刊的替代方法。这些指标最近被路透社采用，并与期刊引文报告中的影响因子一起列出。但是，这些指标的差异是否足以成为文献计量工具箱中有用的补充？Davis（2008）的观点与此相反，他发现医学领域期刊样本的特征因子得分与总引文之间的相关系数为0.95。这个结论是错误的;在这里，我们说明戴维斯屈服于基本的统计谬误。我们提供了一个完整的分析2006年期刊引文报告，并证明有统计和经济上的显着差异提供的信息特征因子和影响因子和总引文。
英文摘要：
The Eigenfactor Metrics provide an alternative way of evaluating scholarly journals based on an iterative ranking procedure analogous to Google's PageRank algorithm. These metrics have recently been adopted by Thomson-Reuters and are listed alongside the Impact Factor in the Journal Citation Reports. But do these metrics differ sufficiently so as to be a useful addition to the bibliometric toolbox? Davis (2008) has argued otherwise, based on his finding of a 0.95 correlation coefficient between Eigenfactor score and Total Citations for a sample of journals in the field of medicine. This conclusion is mistaken; here we illustrate the basic statistical fallacy to which Davis succumbed. We provide a complete analysis of the 2006 Journal Citation Reports and demonstrate that there are statistically and economically significant differences between the information provided by the Eigenfactor Metrics and that provided by Impact Factor and Total Citations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
