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Classification and powerlaws: The logarithmic transformation

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  • Loet Leydesdorff
  • Stephen Bensman

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.

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  • Loet Leydesdorff & Stephen Bensman, 2006. "Classification and powerlaws: The logarithmic transformation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(11), pages 1470-1486, September.
    • Handle: RePEc:bla:jamist:v:57:y:2006:i:11:p:1470-1486
    DOI: 10.1002/asi.20467
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    Cited by:

    1. Junwei Lv & Jing Geng & Xuanhong Xu & Yong Yu & Huajun Fang & Yifan Guo & Shulan Cheng, 2024. "Estimating Cadmium Concentration in Agricultural Soils with ZY1-02D Hyperspectral Data: A Comparative Analysis of Spectral Transformations and Machine Learning Models," Agriculture, MDPI, vol. 14(9), pages 1-17, September.
    2. Zhaozhi Wang & Shoufu Lin & Yang Chen & Oleksii Lyulyov & Tetyana Pimonenko, 2024. "The Digital Economy and Real Economy: The Dynamic Interaction Effect and the Coupling Coordination Degree," Sustainability, MDPI, vol. 16(13), pages 1-20, July.
    3. Bárbara S. Lancho-Barrantes & Vicente P. Guerrero-Bote & Félix Moya-Anegón, 2010. "The iceberg hypothesis revisited," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(2), pages 443-461, November.
    4. Opthof, Tobias & Leydesdorff, Loet, 2010. "Caveats for the journal and field normalizations in the CWTS (“Leiden”) evaluations of research performance," Journal of Informetrics, Elsevier, vol. 4(3), pages 423-430.
    5. Jesper W. Schneider & Birger Larsen & Peter Ingwersen, 2009. "A comparative study of first and all-author co-citation counting, and two different matrix generation approaches applied for author co-citation analyses," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(1), pages 103-130, July.
    6. Loet Leydesdorff, 2011. "“Structuration” by intellectual organization: the configuration of knowledge in relations among structural components in networks of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 499-520, August.
    7. Wang, Ning & Xu, Yan & Wang, Sutong, 2022. "Interpretable boosting tree ensemble method for multisource building fire loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Vicente P. Guerrero-Bote & Felipe Zapico-Alonso & María Eugenia Espinosa-Calvo & Rocío Gómez-Crisóstomo & Félix Moya-Anegón, 2007. "Import-export of knowledge between scientific subject categories: The iceberg hypothesis," Scientometrics, Springer;Akadémiai Kiadó, vol. 71(3), pages 423-441, June.
    9. Hokey Min, 2022. "Examining the Impact of Energy Price Volatility on Commodity Prices from Energy Supply Chain Perspectives," Energies, MDPI, vol. 15(21), pages 1-16, October.
    10. Jonathan Adams & Karen Gurney & Stuart Marshall, 2007. "Profiling citation impact: A new methodology," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(2), pages 325-344, August.
    11. A. J. M. Linmans, 2010. "Why with bibliometrics the Humanities does not need to be the weakest link," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(2), pages 337-354, May.
    12. T. S. Evans & N. Hopkins & B. S. Kaube, 2012. "Universality of performance indicators based on citation and reference counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 473-495, November.
    13. Thelwall, Mike & Wilson, Paul, 2014. "Regression for citation data: An evaluation of different methods," Journal of Informetrics, Elsevier, vol. 8(4), pages 963-971.
    14. Loet Leydesdorff, 2015. "Can intellectual processes in the sciences also be simulated? The anticipation and visualization of possible future states," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2197-2214, December.

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