Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autor principal: Freyer, Leo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2014
Materias:
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
_version_ 1782480692907606016
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