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Computing a journal meta-ranking using paired comparisons and adaptive lasso estimators

Author

Listed:
  • Laura Vana

    (WU Vienna University of Economics and Business)

  • Ronald Hochreiter

    (WU Vienna University of Economics and Business)

  • Kurt Hornik

    (WU Vienna University of Economics and Business)

Abstract

In a “publish-or-perish culture”, the ranking of scientific journals plays a central role in assessing the performance in the current research environment. With a wide range of existing methods for deriving journal rankings, meta-rankings have gained popularity as a means of aggregating different information sources. In this paper, we propose a method to create a meta-ranking using heterogeneous journal rankings. Employing a parametric model for paired comparison data we estimate quality scores for 58 journals in the OR/MS/POM community, which together with a shrinkage procedure allows for the identification of clusters of journals with similar quality. The use of paired comparisons provides a flexible framework for deriving an aggregated score while eliminating the problem of missing data.

Suggested Citation

  • Laura Vana & Ronald Hochreiter & Kurt Hornik, 2016. "Computing a journal meta-ranking using paired comparisons and adaptive lasso estimators," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 229-251, January.
    • Handle: RePEc:spr:scient:v:106:y:2016:i:1:d:10.1007_s11192-015-1772-6
    DOI: 10.1007/s11192-015-1772-6
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    References listed on IDEAS

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    1. Rosenthal, Edward C. & Weiss, Howard J., 2017. "A data envelopment analysis approach for ranking journals," Omega, Elsevier, vol. 70(C), pages 135-147.

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