On the interplay between normalisation, bias, and performance of paper impact metrics

Marcel Dunaiski, Jaco Geldenhuys and Willem Visser

Journal of Informetrics, 2019, vol. 13, issue 1, 270-290

Abstract: We evaluate article-level metrics along two dimensions. Firstly, we analyse metrics’ ranking bias in terms of fields and time. Secondly, we evaluate their performance based on test data that consists of (1) papers that have won high-impact awards and (2) papers that have won prizes for outstanding quality. We consider different citation impact indicators and indirect ranking algorithms in combination with various normalisation approaches (mean-based, percentile-based, co-citation-based, and post hoc rescaling). We execute all experiments on two publication databases which use different field categorisation schemes (author-chosen concept categories and categories based on papers’ semantic information).

Keywords: Impact indicators; Ranking evaluation; Field normalisation; Field bias; Test data (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:13:y:2019:i:1:p:270-290

DOI: 10.1016/j.joi.2019.01.003

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