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Robust rankings: Review of multivariate assessments illustrated by the Shanghai rankings
Defined errors are entered into data collections in order to test their influence on the reliability of multivariate rankings. Random numbers and real ranking data serve as data origins. In the course of data collection small random errors often lead to a switch in ranking, which can influence the g...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Netherlands
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4090748/ https://www.ncbi.nlm.nih.gov/pubmed/25018571 http://dx.doi.org/10.1007/s11192-014-1313-8 |
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author | Freyer, Leo |
author_facet | Freyer, Leo |
author_sort | Freyer, Leo |
collection | PubMed |
description | Defined errors are entered into data collections in order to test their influence on the reliability of multivariate rankings. Random numbers and real ranking data serve as data origins. In the course of data collection small random errors often lead to a switch in ranking, which can influence the general ranking picture considerably. For stabilisation an objective weighting method is evaluated. The robustness of these rankings is then compared to the original forms. Robust forms of the published Shanghai top 100 rankings are calculated and compared to each other. As a result, the possibilities and restrictions of this type of weighting become recognisable. |
format | Online Article Text |
id | pubmed-4090748 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-40907482014-07-10 Robust rankings: Review of multivariate assessments illustrated by the Shanghai rankings Freyer, Leo Scientometrics Article Defined errors are entered into data collections in order to test their influence on the reliability of multivariate rankings. Random numbers and real ranking data serve as data origins. In the course of data collection small random errors often lead to a switch in ranking, which can influence the general ranking picture considerably. For stabilisation an objective weighting method is evaluated. The robustness of these rankings is then compared to the original forms. Robust forms of the published Shanghai top 100 rankings are calculated and compared to each other. As a result, the possibilities and restrictions of this type of weighting become recognisable. Springer Netherlands 2014-05-06 2014 /pmc/articles/PMC4090748/ /pubmed/25018571 http://dx.doi.org/10.1007/s11192-014-1313-8 Text en © The Author(s) 2014 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. |
spellingShingle | Article Freyer, Leo Robust rankings: Review of multivariate assessments illustrated by the Shanghai rankings |
title | Robust rankings: Review of multivariate assessments illustrated by the Shanghai rankings |
title_full | Robust rankings: Review of multivariate assessments illustrated by the Shanghai rankings |
title_fullStr | Robust rankings: Review of multivariate assessments illustrated by the Shanghai rankings |
title_full_unstemmed | Robust rankings: Review of multivariate assessments illustrated by the Shanghai rankings |
title_short | Robust rankings: Review of multivariate assessments illustrated by the Shanghai rankings |
title_sort | robust rankings: review of multivariate assessments illustrated by the shanghai rankings |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4090748/ https://www.ncbi.nlm.nih.gov/pubmed/25018571 http://dx.doi.org/10.1007/s11192-014-1313-8 |
work_keys_str_mv | AT freyerleo robustrankingsreviewofmultivariateassessmentsillustratedbytheshanghairankings |