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

Vana, Laura and Hochreiter, Ronald and Hornik, Kurt ORCID: (2016) Computing a journal meta-ranking using paired comparisons and adaptive lasso estimators. Scientometrics, 106 (1). 229-251. ISSN 1588-2861


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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.

Item Type: Article
Additional Information: To see the final version of this paper please visit the publisher's website. Access to the published version requires a subscription.
Keywords: Adaptive lasso estimators, Journal lists, Meta-ranking, Operations research
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Forschungsinstitute > Rechenintensive Methoden
Version of the Document: Accepted for Publication
Depositing User: Gertraud Novotny
Date Deposited: 24 Jan 2017 14:46
Last Modified: 24 Oct 2019 13:41
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