Scientometric analysis of geostatistics using multivariate methods
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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.
KeywordsDiscriminant Analysis Impact Factor Correct Assignment Total Citation Publication Pattern
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- Garfield, E. (1970), Citation indexing for studying science. Essays of an Information Scientist, 1: 133–138.Google Scholar
- Johnson, R. A., Wichern, D. W. (2002), Applied Multivariate Statistical Analysis. 5th edition. Prentice-Hall, New Jersey.Google Scholar
- Krieger, N., Chen, J. T., Waterman, P. D., Soobader, M. J., Subramanian, S. V., Carson, R. (2002), Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: Does the choice of area-based measure and geographic level matter? The public health disparities geocoding project. American Journal of Epidemiology, 156: 471–482.CrossRefGoogle Scholar
- Krige, D. G. (1952), A statistical analysis of some of the borehole values in the orange free state gold field. Journal of the Chemical and Metallurgical Society of South Africa, 53: 47–64.Google Scholar
- Sichel, H. S. (1952), New methods in the statistical evaluation of mine sampling data. Transactions of the Institution of Mining and Metallurgy.Google Scholar
- Zhou, F., Liu, Y., Guo, H. C. (2006), Application of multivariate statistical methods to the water quality assessment of the watercourses in the Northwestern New Territories, Hong Kong. Environmental Monitoring and Assessment, DOI 10.1007/s10661-006-9497-x.Google Scholar