图书情报工作 ›› 2013, Vol. 57 ›› Issue (10): 110-115.DOI: 10.7536/j.issn.0252-3116.2013.10.017

• 情报研究 • 上一篇    下一篇

基于大数据的h指数及其衍生指数的探索性因子分析研究

阎素兰, 彭秋茹, 张超群, 杨波   

  1. 南京农业大学
  • 收稿日期:2013-02-18 修回日期:2013-04-20 出版日期:2013-05-20 发布日期:2013-05-20
  • 通讯作者: 杨波,南京农业大学信息科学技术学院讲师,E-mail:boyang@njau.edu.cn。
  • 作者简介:阎素兰,南京农业大学信息科学技术学院讲师;彭秋茹,南京农业大学情报学在读硕士研究生;张超群,南京农业大学图书馆学在读硕士研究生;杨波,南京农业大学信息科学技术学院讲师,E-mail:boyang@njau.edu.cn。

Exploratory Factor Analysis on H-Index and its Derivative Indexes Based on Big Data

Yan Sulan, Peng Qiuru, Zhang Chaoqun, Yang Bo   

  1. School of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095
  • Received:2013-02-18 Revised:2013-04-20 Online:2013-05-20 Published:2013-05-20

摘要:

h指数自2005年被Jorge E. Hirsch提出后,因其具有计算简单和适用范围广等特点而引发了学术界的广泛关注。许多研究者在h指数的基础上,对h指数进行修正,并提出了多种衍生指数。为深入了解这些同源性衍生指数间的关系,评估它们在不同层次人才评价中的应用效果,选择15种主要类h指数,以2008-2011年SCIE收录的农业科学领域114 643篇论文为研究对象,采用探索性因子分析方法对15种指数进行相关性分析,结果表明在大数据情况下A指数和m指数与作者论文质量密切相关;g指数、hw指数和R指数能更好地评价高产高被引作者;hw指数和χ指数在评价高产低被引作者时效果较好;π指数、χ指数、m指数、A指数、hw指数、e指数对低产高被引作者评价效果较好。

关键词: h指数, 类h指数, 人才评价, 因子分析

Abstract:

Since Jorge E. Hirsch proposed the h index in 2005, this indictor has been focused on by academia because of its characteristic of simple calculation and wide application. Many researchers made a lot of work to revise it, and proposed many types of derivative h indexes. In order to find out clearly the homology relationship among these indictors, this article selected 114 643 articles of ESI Agricultural Science from SCIE database during 2008 to 2011, to analyze their correlation by Exploratory Factor Analysis. The results showed that: in the case of big data, A Index and m Index are closely related to the quality of articles; g Index, hw Index and R Index are better to evaluate higher production and higher citation authors; hw Index and χ Index are better for higher production and lower citation authors; π Index, χ Index, m Index, A Index, hw Index, e Index are better for lower production and higher citation authors.

Key words: H-Index, derivative h indexes, personnel evaluation, factor analysis

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