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Scientometrics

, Volume 73, Issue 3, pp 265–279 | Cite as

Scientometric analysis of geostatistics using multivariate methods

  • Feng Zhou
  • Huai-Cheng Guo
  • Yuh-Shan Ho
  • Chao-Zhong Wu
Article

Abstract

Multivariate methods were successfully employed in a comprehensive scientometric analysis of geostatistics research, and the publications data for this research came from the Science Citation Index and spanned the period from 1967 to 2005. Hierarchical cluster analysis (CA) was used in publication patterns based on different types of variables. A backward discriminant analysis (DA) with appropriate statistical tests was then conducted to confirm CA results and evaluate the variations of various patterns. For authorship pattern, the 50 most productive authors were classified by CA into 4 groups representing different levels, and DA produced 92.0% correct assignment with high reliability. The discriminant parameters were mean impact factor (MIF), annual citations per publication (ACPP), and the number of publications by the first author, for country/region pattern, CA divided the top 50 most productive countries/regions into 4 groups with 95.9% correct assignments, and the discriminant parameters were MIF, ACCP, and independent publication (IP); for institute pattern, 3 groups were identified from the top 50 most productive institutes with nearly 88.0% correct assignment, and the discriminant parameters were MIF, ACCP, IP, and international collaborative publication; last, for journal pattern, the top 50 most productive journals were classified into 3 groups with nearly 98.0% correct assignment, and its discriminant parameters were total citations, impact factor and ACCP. Moreover, we also analyzed general patterns for publication document type, language, subject category, and publication growth.

Keywords

Discriminant Analysis Impact Factor Correct Assignment Total Citation Publication Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Feng Zhou
    • 1
  • Huai-Cheng Guo
    • 1
    • 3
  • Yuh-Shan Ho
    • 1
  • Chao-Zhong Wu
    • 2
  1. 1.College of Environmental SciencesPeking UniversityBeijingP. R. China
  2. 2.Intelligent Transport System Research CenterWuhan University of TechnologyWuhanP. R. China
  3. 3.College of Environmental SciencesPeking UniversityBeijingChina

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