Alternatives to the Journal Impact Factor: I3 and the Top-10% (or Top-25%?) of the Most-Highly Cited Papers

TL;DR

This paper discusses alternatives to the Journal Impact Factor, proposing the I3 indicator and top citation percentiles as more normalized and statistically robust measures of journal and article impact.

Contribution

It introduces the I3 metric and the use of top-10% citation counts as novel, normalized, and statistically testable alternatives to traditional impact factors.

Findings

I3 provides a normalized impact measure accounting for field differences.

Top-10% citation indicator offers a simple, statistically testable impact assessment.

These alternatives address skewness and normalization issues in citation analysis.

Abstract

Journal Impact Factors (IFs) can be considered historically as the first attempt to normalize citation distributions by using averages over two years. However, it has been recognized that citation distributions vary among fields of science and that one needs to normalize for this. Furthermore, the mean-or any central-tendency statistics-is not a good representation of the citation distribution because these distributions are skewed. Important steps have been taken to solve these two problems during the last few years. First, one can normalize at the article level using the citing audience as the reference set. Second, one can use non-parametric statistics for testing the significance of differences among ratings. A proportion of most-highly cited papers (the top-10% or top-quartile) on the basis of fractional counting of the citations may provide an alternative to the current IF. This indicator is intuitively simple, allows for statistical testing, and accords with the state of the art.