McCall's Area Transformation versus the Integrated Impact Indicator (I3)

TL;DR

This paper compares McCall's Area Transformation and the Integrated Impact Indicator (I3) for analyzing skewed citation distributions, assessing their relative effectiveness using data from mathematical psychology journals.

Contribution

It replicates prior studies and evaluates the relative performance of McCall's transformation and I3 in journal impact analysis.

Findings

Both approaches are similar in handling skewed citation data.

I3 provides a flexible framework with percentile-based variants.

The study offers insights into the suitability of each method for impact assessment.

Abstract

In a study entitled "Skewed Citation Distributions and Bias Factors: Solutions to two core problems with the journal impact factor," Mutz & Daniel (2012) propose (i) McCall's (1922) Area Transformation of the skewed citation distribution so that this data can be considered as normally distributed (Krus & Kennedy, 1977), and (ii) to control for different document types as a co-variate (Rubin, 1977). This approach provides an alternative to Leydesdorff & Bornmann's (2011) Integrated Impact Indicator (I3). As the authors note, the two approaches are akin. Can something be said about the relative quality of the two approaches? To that end, I replicated the study of Mutz & Daniel for the 11 journals in the Subject Category "mathematical psychology," but using additionally I3 on the basis of continuous quantiles (Leydesdorff & Bornmann, in press) and its variant PR6 based on the six percentile rank classes distinguished by Bornmann & Mutz (2011) as follows: the top-1%, 95-99%, 90-95%, 75-90%, 50-75%, and bottom-50%.