  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%.
