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Leydesdorff, L and Bensman, S (2006)

Classification and power laws: The logarithmic transformation

Journal of the American Society for Information Science and Technology 57(11), pp. 1470-1486.

ISSN/ISBN: 1532-2882 DOI: 10.1002/asi.20467

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.

@article{, title={Classification and powerlaws: The logarithmic transformation}, author={Leydesdorff, Loet and Bensman, Stephen}, journal={Journal of the American Society for Information Science and Technology}, volume={57}, number={11}, pages={1470--1486}, year={2006}, ISSN={1532-2882}, publisher={Wiley Online Library}, DOI={10.1002/asi.20467}, }

Reference Type: Journal Article

Subject Area(s): Statistics