Impact Factors and the Central Limit Theorem: Why citation averages are scale dependent

Manolis Antonoyiannakis

Journal of Informetrics, 2018, vol. 12, issue 4, 1072-1088

Abstract: Citation averages, and Impact Factors (IFs) in particular, are sensitive to sample size. Here, we apply the Central Limit Theorem to IFs to understand their scale-dependent behavior. For a journal of n randomly selected papers from a population of all papers, we expect from the Theorem that its IF fluctuates around the population average μ, and spans a range of values proportional to σ/n, where σ2 is the variance of the population's citation distribution. The 1/n dependence has profound implications for IF rankings: The larger a journal, the narrower the range around μ where its IF lies. IF rankings therefore allocate an unfair advantage to smaller journals in the high IF ranks, and to larger journals in the low IF ranks. As a result, we expect a scale-dependent stratification of journals in IF rankings, whereby small journals occupy the top, middle, and bottom ranks; mid-sized journals occupy the middle ranks; and very large journals have IFs that asymptotically approach μ. We obtain qualitative and quantitative confirmation of these predictions by analyzing (i) the complete set of 166,498 IF & journal-size data pairs in the 1997–2016 Journal Citation Reports of Clarivate Analytics, (ii) the top-cited portion of 276,000 physics papers published in 2014–2015, and (iii) the citation distributions of an arbitrarily sampled list of physics journals. We conclude that the Central Limit Theorem is a good predictor of the IF range of actual journals, while sustained deviations from its predictions are a mark of true, non-random, citation impact. IF rankings are thus misleading unless one compares like-sized journals or adjusts for these effects. We propose the Φ index, a rescaled IF that accounts for size effects, and which can be readily generalized to account also for different citation practices across research fields. Our methodology applies to other citation averages that are used to compare research fields, university departments or countries in various types of rankings.

Keywords: Science of Science; Scholarly Publishing; Impact Factors; Journal size; Central Limit Theorem (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157718301238
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:12:y:2018:i:4:p:1072-1088

DOI: 10.1016/j.joi.2018.08.011

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 ().