数据统计稀少引起的对等度指标的解释和推断 (Interpretation and inference for altmetric indicators arising from sparse data statistics)

In 2018 Bornmann and Haunschild (2018a) introduced a new indicator called the Mantel-Haenszel quotient (MHq) to measure alternative metrics (or altmetrics) of scientometric data. In this article we review the Mantel-Haenszel statistics, point out two errors in the literature, and introduce a new indicator. First, we correct the interpretation of MHq and mention that it is still a meaningful indicator. Second, we correct the variance formula for MHq, which leads to narrower confidence intervals. A simulation study shows the superior performance of our variance estimator and confidence intervals. Since MHq does not match its original description in the literature, we propose a new indicator, the Mantel-Haenszel row risk ratio (MHRR), to meet that need. Interpretation and statistical inference for MHRR are discussed. For both MHRR and MHq, a value greater (less) than one means performance is better (worse) than in the reference set called the world.

翻译：2018年,Bornmann和Haunschild(2018年a)引入了一个新的指标,称为Mantel-Haenszel 商数(MHq),以测量科学数据的其他指标(或等量)。在本篇文章中,我们审查了Mantel-Haenszel的统计数据,指出了文献中的两个错误,并引入了一个新的指标。首先,我们纠正了对MHq的解释,并提到它仍然是一个有意义的指标。第二,我们纠正了MHq的差异公式,这导致信任间隔的缩小。模拟研究显示了我们差异估计和信任间隔的优异性表现。由于MHq与文献中的原始描述不匹配,我们提出了一个新的指标,即Mantel-Haenszel行风险比率(MHRR),以满足这一需要。讨论了MHRR的解读和统计推论。对于MHRR和MHq来说,一个更大的(低)值意味着业绩优于称为世界的参考组。