Classification and powerlaws: The logarithmic transformation: Research Articles

Authors:
Loet Leydesdorff;Stephen Bensman
Affiliations:
Amsterdam School of Communications Research, University of Amsterdam, Kloveniersburgwal 48, 1012 CX Amsterdam, The Netherlands;LSU Libraries, Louisiana State University, Baton Rouge, LA 70803–3300
Venue:
Journal of the American Society for Information Science and Technology
Year:
2006

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Abstract

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. © 2006 Wiley Periodicals, Inc.