Citation bias and other determinants of citation in biomedical research: findings from six citation networks

M.J.E. Urlings*, B. Duyx, G.M.H. Swaen, L.M. Bouter, M.P. Zeegers

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Objectives: When the probability of being cited depends on the outcome of that study, this is called citation bias. The aim of this study is to assess the determinants of citation and how these compare across six different biomedical research fields.Study Design and Setting: Citation network analyses were performed for six biomedical research questions. After identifying all relevant publications, all potential citations were mapped together with the actually performed citations in each network. As determinants of citation we assessed the following: study outcome, study design, sample size, journal impact factor, gender, affiliation, authority and continent of the corresponding author, funding source, title of the publication, number of references, and self-citation. Random effect logistic regression analysis was used to assess these factors.Results: Four out of six networks showed evidence for citation bias. Self-citation, authority of the author, and journal impact factor were also positively associated with the probability of citation in all networks. Conclusion: The probability of being cited seems associated with positive study outcomes, the authority of its authors, and the journal in which that article is published. In addition, each network showed specific characteristics that impact the citation dynamics and that need to be considered when performing and interpreting citation analyses. ? 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Original languageEnglish
Pages (from-to)71-78
Number of pages8
JournalJournal of Clinical Epidemiology
Volume132
DOIs
Publication statusPublished - 1 Apr 2021

Keywords

  • Citation bias
  • Citation network analyses
  • Questionable research practices
  • Biomedical research
  • H-index

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