Screening the most highly cited papers in longitudinal bibliometric studies and systematic literature reviews of a research field or journal: Widespread used metrics vs a percentile citation-based approach

Gerson Pech and Catarina Delgado

Journal of Informetrics, 2021, vol. 15, issue 3

Abstract: There is a literature gap regarding the period representativeness bias associated with sample selection in longitudinal bibliometric studies. The purpose of this paper is to analyse and compare, in terms of period representativeness, the common methods used for selecting a sample of the highly impactful papers in a field/ journal. Using 92 593 papers (Information Science & Library Science area, 1977–2016), we compared, in terms of the number of papers/year, samples of the 100 most impactful papers, obtained with different selection options. We repeated the analysis also for Top500, Top2000, and Top20000. This study shows that the frequently used metrics to compare the impact of papers and to select a sample of "most impactful papers" published in each year and each field may privilege specific periods while neglecting others. The main result of our study is that the percentile citation-based method reduces this "year of publication" representativeness bias. This paper draws attention to the importance of the sample selection, in bibliometric studies, and to the period representativeness bias associated with different choices to select the "most impactful papers".

Keywords: Citation analysis; Percentile method; Most highly cited papers; Bibliometric/longitudinal studies; Research impact; Sample selection bias (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157721000328
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:15:y:2021:i:3:s1751157721000328

DOI: 10.1016/j.joi.2021.101161

Access Statistics for this article

Journal of Informetrics is currently edited by Leo Egghe

More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().