  Logarithmic transformation of the data has been recommended by the literature
in the case of highly skewed distributions such as those commonly found in
information science. The purpose of the transformation is to make the data
conform to the lognormal law of error for inferential purposes. How does this
transformation affect the analysis? We factor analyze and visualize the
citation environment of the Journal of the American Chemical Society (JACS)
before and after a logarithmic transformation. The transformation strongly
reduces the variance necessary for classificatory purposes and therefore is
counterproductive to the purposes of the descriptive statistics. We recommend
against the logarithmic transformation when sets cannot be defined
unambiguously. The intellectual organization of the sciences is reflected in
the curvilinear parts of the citation distributions, while negative powerlaws
fit excellently to the tails of the distributions.
