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

Laura Vana Gür, Ronald Hochreiter, Kurt Hornik

Publication: Scientific journalJournal articlepeer-review

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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.
Original languageEnglish
Pages (from-to)229 - 251
Issue number1
Publication statusPublished - 2016

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