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

Laura Vana (), Ronald Hochreiter () and Kurt Hornik ()
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Laura Vana: WU Vienna University of Economics and Business
Kurt Hornik: WU Vienna University of Economics and Business

Scientometrics, 2016, vol. 106, issue 1, No 13, 229-251

Abstract: 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.

Keywords: Adaptive lasso estimators; Journal lists; Meta-ranking; Operations research (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-015-1772-6

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