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

Vana, Laura and Hochreiter, Ronald and Hornik, Kurt ORCID: https://orcid.org/0000-0003-4198-9911 (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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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.

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
Related URLs:	Publisher
FIDES Link:	https://bach.wu.ac.at/d/research/results/73271/
URI:	https://epub.wu.ac.at/id/eprint/5392

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