Cluster analysis of top 200 universities in Mathematics
University rankings are becoming a vital performance assessment for higher learning institutions worldwide. Besides the overall rankings, the universities are also ranked by subjects serving as comprehensive guide to discover the specialist strengths of universities worldwide by highlighting top 200...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/56278/1/Cluster%20analysis%20of%20top%20200%20universities%20in%20Mathematics.pdf |
| _version_ | 1796976445920116736 |
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| author | Gopal, Kathiresan Shitan, Mahendran |
| author_facet | Gopal, Kathiresan Shitan, Mahendran |
| author_sort | Gopal, Kathiresan |
| collection | UPM |
| description | University rankings are becoming a vital performance assessment for higher learning institutions worldwide. Besides the overall rankings, the universities are also ranked by subjects serving as comprehensive guide to discover the specialist strengths of universities worldwide by highlighting top 200 universities for a range of 30 individual popular subjects. Data for this ranking purpose consist four variables namely the academic reputation, employer reputation, citation per paper and H-index citations. In this ranking, universities are ranked according to their overall score calculated from linear combination of the aforementioned variables and their respective weightings. As the existing ranking technique based on overall score appears to be simple and since the rankings data are of multivariate nature, therefore it is possible to use multivariate statistical technique like cluster analysis. Agglomerative hierarchical cluster analysis of top 200 QS ranked universities by Mathematics subject area 2014 has been performed to obtain natural clustering of the universities in an objective manner. The agreement between cluster analysis and existing QS rankings is verified and it is suggested that the distance between universities can be used as an alternative measure to rank universities. Cluster analysis applied on the same variables would serve as an alternative way to rank universities and to look at the rankings in a different perspective. |
| first_indexed | 2024-03-06T09:25:55Z |
| format | Conference or Workshop Item |
| id | upm.eprints-56278 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:25:55Z |
| publishDate | 2015 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-562782017-07-31T04:19:50Z http://psasir.upm.edu.my/id/eprint/56278/ Cluster analysis of top 200 universities in Mathematics Gopal, Kathiresan Shitan, Mahendran University rankings are becoming a vital performance assessment for higher learning institutions worldwide. Besides the overall rankings, the universities are also ranked by subjects serving as comprehensive guide to discover the specialist strengths of universities worldwide by highlighting top 200 universities for a range of 30 individual popular subjects. Data for this ranking purpose consist four variables namely the academic reputation, employer reputation, citation per paper and H-index citations. In this ranking, universities are ranked according to their overall score calculated from linear combination of the aforementioned variables and their respective weightings. As the existing ranking technique based on overall score appears to be simple and since the rankings data are of multivariate nature, therefore it is possible to use multivariate statistical technique like cluster analysis. Agglomerative hierarchical cluster analysis of top 200 QS ranked universities by Mathematics subject area 2014 has been performed to obtain natural clustering of the universities in an objective manner. The agreement between cluster analysis and existing QS rankings is verified and it is suggested that the distance between universities can be used as an alternative measure to rank universities. Cluster analysis applied on the same variables would serve as an alternative way to rank universities and to look at the rankings in a different perspective. IEEE 2015 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/56278/1/Cluster%20analysis%20of%20top%20200%20universities%20in%20Mathematics.pdf Gopal, Kathiresan and Shitan, Mahendran (2015) Cluster analysis of top 200 universities in Mathematics. In: 2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC2015), 19-20 May 2015, Ipoh, Perak. (pp. 408-413). 10.1109/ISMSC.2015.7594089 |
| spellingShingle | Gopal, Kathiresan Shitan, Mahendran Cluster analysis of top 200 universities in Mathematics |
| title | Cluster analysis of top 200 universities in Mathematics |
| title_full | Cluster analysis of top 200 universities in Mathematics |
| title_fullStr | Cluster analysis of top 200 universities in Mathematics |
| title_full_unstemmed | Cluster analysis of top 200 universities in Mathematics |
| title_short | Cluster analysis of top 200 universities in Mathematics |
| title_sort | cluster analysis of top 200 universities in mathematics |
| url | http://psasir.upm.edu.my/id/eprint/56278/1/Cluster%20analysis%20of%20top%20200%20universities%20in%20Mathematics.pdf |
| work_keys_str_mv | AT gopalkathiresan clusteranalysisoftop200universitiesinmathematics AT shitanmahendran clusteranalysisoftop200universitiesinmathematics |