題名: Scientometric analysis of geostatistics using multivariate methods 作者: Zhou, F.;Guo, H.C.;Ho, Y.S.;Wu, C.Z.
貢獻者: Department of Biotechnology 日期: 2007
上傳時間: 2009-12-15T05:25:02Z 出版者: Asia University
摘要: 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.
