
doi: 10.3846/bme.2015.260
University rankings are extremely important not only for future student, but also for universities themselves. They have a large impact on the institutions of higher education. A lot of universities believe, that rankings help them to maintain and create a reputation. Ranking systems function as some kind of fashion arena, where universities make comparisons between themselves. Universities want to improve their position in published classifications, so very often they try to change their policy and strategy. They also try to influence the ranking indicators, for example by hiring Nobel Prize winners. Therefore, there is an increasing need for reliable and transparent information about schools. However universities need not only statistical data, but also the tools, which will be useful in their comparisons and evaluations. The article presents the possibility of using one of the methods of graphic presentation of multidimensional empirical data structure, so called RGM, proposed by M. Rybaczuk. Thanks to this method universities could easily compare one another. They also could identify the fields of their activities, in which they are able to be better. The proposed way of graphical presentation of the universities could be a useful addition to traditional rankings, which just show us a lists of schools from the best to the worst.
university, ranking, HF5001-6182, higher education, Management. Industrial management, Business, Articles, strategy, HD28-70, management
university, ranking, HF5001-6182, higher education, Management. Industrial management, Business, Articles, strategy, HD28-70, management
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 11 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
