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Scientometrics

, Volume 118, Issue 2, pp 653–671 | Cite as

Large enough sample size to rank two groups of data reliably according to their means

  • Zhesi Shen
  • Liying Yang
  • Zengru Di
  • Jinshan WuEmail author
Article
  • 33 Downloads

Abstract

Often we need to compare two sets of data, say X and Y, and often via comparing their means \(\mu _{X}\) and \(\mu _{Y}\). However, when two sets are highly overlapped (say for example \(\sqrt{\sigma ^{2}_{X}+\sigma ^{2}_{Y}}\gg \left| \mu _{X}-\mu _{Y}\right|\)), ranking the two sets according to their means might not be reliable. Based on the observation that replacing the one-by-one comparison, where we take one sample from each set at a time and compare the two samples, with the \(K_{X}\)-by-\(K_{Y}\) comparison, where we take \(K_{X}\) samples \(\left\{ x_{1}, x_{2}, \ldots , x_{K_{X}}\right\}\) from one set and \(K_{Y}\) samples \(\left\{ y_{1}, y_{2},\ldots , y_{K_{X}}\right\}\) from the other set at a time and compare the averages \(\frac{\sum _{j=1}^{K_{X}}x_{j}}{K_{X}}\) and \(\frac{\sum _{j=1}^{K_{Y}}y_{j}}{K_{Y}}\), reduces the overlap and thus improves the reliability, we propose a definition of the minimum representative size \(\kappa\) of each set for comparing sets by requiring roughly speaking \(\sqrt{\sigma ^{2}_{K_X}+\sigma ^{2}_{K_Y}}\ll \left| \mu _{X}-\mu _{Y}\right|\)). Applied to journal comparison, this minimum representative size \(\kappa\) might be used as a complementary index to the journal impact factor (JIF) to indicate a measure of reliability of comparing two journals using their JIFs. Generally, this idea of minimum representative size can be used when any two sets of data with overlapping distributions are compared.

Keywords

Journal impact factor Minimum representative size Bootstrap sampling 

Notes

Acknowledgements

We thank Jianlin Zhou, Per Ahlgren, Ludo Waltman and Lawrence Smolinsky for valuable discussions since this paper’s early version (Shen et al. 2017). We would also like to thank the anonymous referees for their suggestions and criticisms, which have greatly improved the paper’s presentation. This work was supported by the NSFC under Grant No. 61374175, the China Postdoctoral Science Foundation under Grant 2017 M620944, and Fundamental Research Funds for the Central Universities.

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.National Science LibraryChinese Academy of SciencesBeijingPeople’s Republic of China
  2. 2.School of Systems ScienceBeijing Normal UniversityBeijingPeople’s Republic of China

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