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A comparative study of first and all-author co-citation counting, and two different matrix generation approaches applied for author co-citation analyses

Author

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  • Jesper W. Schneider

    (Royal School of Library and Information Science)

  • Birger Larsen

    (Royal School of Library and Information Science)

  • Peter Ingwersen

    (Royal School of Library and Information Science)

Abstract

Aim The present article contributes to the current methodological debate concerning author co-citation analyses. (ACA) The study compares two different units of analyses, i.e. first- versus inclusive all-author co-citation counting, as well as two different matrix generation approaches, i.e. a conventional multivariate and the so-called Drexel approach, in order to investigate their influence upon mapping results. The aim of the present study is therefore to provide more methodological awareness and empirical evidence concerning author co-citation studies. Method The study is based on structured XML documents extracted from the IEEE collection. These data allow the construction of ad-hoc citation indexes, which enables us to carry out the hitherto largest all-author co-citation study. Four ACA are made, combining the different units of analyses with the different matrix generation approaches. The results are evaluated quantitatively by means of multidimensional scaling, factor analysis, Procrustes and Mantel statistics. Results The results show that the inclusion of all cited authors can provide a better fit of data in two-dimensional mappings based on MDS, and that inclusive all-author co-citation counting may lead to stronger groupings in the maps. Further, the two matrix generation approaches produce maps that have some resemblances, but also many differences at the more detailed levels. The Drexel approach produces results that have noticeably lower stress values and are more concentrated into groupings. Finally, the study also demonstrates the importance of sparse matrices and their potential problems in connection with factor analysis. Conclusion We can confirm that inclusive all-ACA produce more coherent groupings of authors, whereas the present study cannot clearly confirm previous findings that first-ACA identifies more specialties, though some vague indication is given. Most crucially, strong evidence is given to the determining effect that matrix generation approaches have on the mapping of author co-citation data and thus the interpretation of such maps. Evidence is provided for the seemingly advantages of the Drexel approach.

Suggested Citation

  • Jesper W. Schneider & Birger Larsen & Peter Ingwersen, 2009. "A comparative study of first and all-author co-citation counting, and two different matrix generation approaches applied for author co-citation analyses," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(1), pages 103-130, July.
    • Handle: RePEc:spr:scient:v:80:y:2009:i:1:d:10.1007_s11192-007-2019-y
    DOI: 10.1007/s11192-007-2019-y
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    References listed on IDEAS

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    Cited by:

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    2. Strotmann, Andreas & Zhao, Dangzhi, 2010. "Combining commercial citation indexes and open-access bibliographic databases to delimit highly interdisciplinary research fields for citation analysis," Journal of Informetrics, Elsevier, vol. 4(2), pages 194-200.
    3. Mora, Luca & Deakin, Mark & Reid, Alasdair, 2019. "Combining co-citation clustering and text-based analysis to reveal the main development paths of smart cities," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 56-69.
    4. Jun-Ping Qiu & Ke Dong & Hou-Qiang Yu, 2014. "Comparative study on structure and correlation among author co-occurrence networks in bibliometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1345-1360, November.
    5. Song Yanhui & Wu Lijuan & Qiu Junping, 2021. "A comparative study of first and all-author bibliographic coupling analysis based on Scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1125-1147, February.
    6. Jeong, Yoo Kyung & Song, Min & Ding, Ying, 2014. "Content-based author co-citation analysis," Journal of Informetrics, Elsevier, vol. 8(1), pages 197-211.
    7. Yi Bu & Tian-yi Liu & Win-bin Huang, 2016. "MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 143-166, July.
    8. Wang, Feifei & Jia, Chenran & Wang, Xiaohan & Liu, Junwan & Xu, Shuo & Liu, Yang & Yang, Chenyuyan, 2019. "Exploring all-author tripartite citation networks: A case study of gene editing," Journal of Informetrics, Elsevier, vol. 13(3), pages 856-873.

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