A Comparative Analysis on Multiple Authorship Counting for Author Co-citation Analysis

Lee, Jae Yun (이재윤); Eunkyung Chung (정은경)

doi:10.3743/KOSIM.2014.31.2.057

A Comparative Analysis on Multiple Authorship Counting for Author Co-citation Analysis

Journal of the Korean Society for Information Management
Abbr : JKOSIM
2014, 31(2), pp.57~77
DOI : 10.3743/KOSIM.2014.31.2.057
Publisher : 한국정보관리학회
Research Area : Interdisciplinary Studies > Library and Information Science
Received : May 20, 2014
Accepted : June 17, 2014
Published : June 30, 2014

Lee, Jae Yun ¹, Eunkyung Chung ²

¹명지대학교
²이화여자대학교

Accredited

ABSTRACT

As co-authorship has been prevalent within science communities, counting the credit of co-authors appropriately is an important consideration, particularly in the context of identifying the knowledge structure of fields with author-based analysis. The purpose of this study is to compare the characteristics of co-author credit counting methods by utilizing correlations, multidimensional scaling, and pathfinder networks. To achieve this purpose, this study analyzed a dataset of 2,014 journal articles and 3,892 cited authors from the Journal of the Architectural Institute of Korea: Planning & Design from 2003 to 2008 in the field of Architecture in Korea. In this study, six different methods of crediting co-authors are selected for comparative analyses. These methods are first-author counting (m1), straight full counting (m2), and fractional counting (m3), proportional counting with a total score of 1 (m4), proportional counting with a total score between 1 and 2 (m5), and first-author-weighted fractional counting (m6). As shown in the data analysis, m1 and m2 are found as extreme opposites, since m1 counts only first authors and m2 assigns all co-authors equally with a credit score of 1. With correlation and multidimensional scaling analyses, among five counting methods (from m2 to m6), a group of counting methods including m3, m4, and m5 are found to be relatively similar. When the knowledge structure is visualized with pathfinder network, the knowledge structure networks from different counting methods are differently presented due to the connections of individual links. In addition, the internal validity shows that first-author-weighted fractional counting (m6) might be considered a better method to author clustering. Findings demonstrate that different co-author counting methods influence the network results of knowledge structure and a better counting method is revealed for author clustering.

KEYWORDS

author co-citation analysis, multiple authorship, co-authorship, intellectual structure, authorship counting, pathfinder network, multidimensional scaling

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Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

A Comparative Analysis on Multiple Authorship Counting for Author Co-citation Analysis

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* References for papers published after 2023 are currently being built.

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

A Comparative Analysis on Multiple Authorship Counting for Author Co-citation Analysis

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