In order to search for drivers and barriers to regional participation in student mobility, unearthing more hidden similarities and differences between European regions, a k-means cluster analysis is used. K-means is a method to separate a dataset into groups, in which observations in the same group are as similar as possible and in different groups as dissimilar as possible. The differentiation into clusters is indeed meaningful, as the analysis generates five groups of regions of different sizes and with different variations around the mean, but almost no overlap:
Cluster 1: the mainstream strugglers
Cluster 2: the mainstream performers
Cluster 3: the constrained dependents
Cluster 4: the specialized attractors
Cluster 5: the superstars
Theme(s): Population and living conditions - Education - Population and Living Conditions
| Spatial Extent | Nomenclature | ||
|---|---|---|---|
| name | version | level | |
| EU27+4EFTA+UK | NUTS | 2016 | 2 |
Cluster analysis
None!
None