Computing a journal meta-ranking using paired comparisons and adaptive lasso estimators
| Autor: | Kurt Hornik, Ronald Hochreiter, Laura Vana |
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| Rok vydání: | 2015 |
| Předmět: |
Adaptive lasso estimators
Journal lists Meta-ranking Operations research Computer science media_common.quotation_subject 05 social sciences General Social Sciences Estimator Library and Information Sciences 050905 science studies Missing data computer.software_genre Computer Science Applications Identification (information) Lasso (statistics) Ranking 0502 economics and business Parametric model Range (statistics) Quality (business) Data mining 0509 other social sciences computer 050203 business & management media_common |
| Zdroj: | Scientometrics. 106:229-251 |
| ISSN: | 1588-2861 0138-9130 |
| DOI: | 10.1007/s11192-015-1772-6 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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