引用
统计
引文分析
冲击系数
计量经济学
样本量测定
标准差
蒙特卡罗方法
领域(数学)
索引(排版)
引文索引
航程(航空)
引文影响
文献计量学
极限(数学)
比例(比率)
数学
样品(材料)
计算机科学
灵敏度(控制系统)
大样本
精算学
随机变量
科学引文索引
标准误差
作者
Manolis Antonoyiannakis
摘要
Abstract The impact factor (IF), despite its widespread use, suffers from well-known biases that remain incompletely addressed in practice—most notably its sensitivity to journal size and its lack of field normalization. Because of size sensitivity, a randomly formed journal of n papers can attain a range of IF values that decreases sharply with size, as ∼1/n. The central limit theorem, which underlies this effect, also allows us to correct for it by standardizing citation averages for scale and field in a manner analogous to calculating the z-score in statistics. I thus introduce the Φ (Phi) index, defined as Φ=f−μn/σ, where f is a journal’s average citation count (akin to the IF), n is its publication count, and μ and σ are the mean and standard deviation of citations in its field. Applying the Φ index to 12,173 journals in Clarivate’s Journal Citation Reports, I obtain rankings that correct for size bias and elevate journals from underrepresented fields, such as mathematics, law, and history. I validate the Φ index via a Monte Carlo random sample test, which I propose as a standard diagnostic for any citation indicator. The methodology extends readily to departments, universities, and countries.
科研通智能强力驱动
Strongly Powered by AbleSci AI