McCall的面积转换与综合影响指标（I3）
McCall's Area Transformation versus the Integrated Impact Indicator (I3)
摘要：
在一项名为“偏斜引用分布和偏差因素：解决期刊影响因子的两个核心问题，”Mutz & Daniel（2012）提出（i）McCall（1922）对偏态引用分布的面积变换，以便该数据可以被视为正态分布（Krus & Kennedy，1977），以及（ii）控制不同的文档类型作为协变量（Rubin，1977）。这种方法提供了Leydesdorff & Bornmann（2011）综合影响指标（I3）的替代方法。正如作者所指出的，这两种方法是相似的。可以谈谈这两种方法的相对质量吗？为此，我复制了Mutz和Daniel的研究，用于主题类别“数学心理学”的11种期刊，但在连续分位数的基础上额外使用了I3（Leydesdorff & Bornmann，in press）及其变体PR 6基于Bornmann & Mutz（2011）区分的六个百分位等级，如下所示：顶部-1%，95- 99%，90- 95%，75- 90%，50- 75%，底部-50%。
英文摘要：
In a study entitled "Skewed Citation Distributions and Bias Factors: Solutions to two core problems with the journal impact factor," Mutz & Daniel (2012) propose (i) McCall's (1922) Area Transformation of the skewed citation distribution so that this data can be considered as normally distributed (Krus & Kennedy, 1977), and (ii) to control for different document types as a co-variate (Rubin, 1977). This approach provides an alternative to Leydesdorff & Bornmann's (2011) Integrated Impact Indicator (I3). As the authors note, the two approaches are akin. Can something be said about the relative quality of the two approaches? To that end, I replicated the study of Mutz & Daniel for the 11 journals in the Subject Category "mathematical psychology," but using additionally I3 on the basis of continuous quantiles (Leydesdorff & Bornmann, in press) and its variant PR6 based on the six percentile rank classes distinguished by Bornmann & Mutz (2011) as follows: the top-1%, 95-99%, 90-95%, 75-90%, 50-75%, and bottom-50%.
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。
