Mapping Diversity: Developing a European Classification of ... - U-Map

The clustering is based on the correlation matrix of the scores on the questions (Kendall’s tau). The
signifi cant correlations were mapped to fi nd out whether clear clusters emerge 10 . Using Kendall’s
tau, ten combinations emerge as statistically signifi cant.
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Table 12: Correlations between dimensions
dimension dimension Kendall’s tau
11 12 0.82**
2 9 0.49*
4 10 0.48*
8 14 0.80*
11 13 0.46*
6 9 -0.71**
2 14 -0.69**
5 10 -0.54**
2 11 -0.53*
5 11 -0.54*
7 11 -0.53*
9 13 -0.41*
** signifi cant at 0.01 level
* signifi cant at 0.05 level
If the dimension public character (11) is mentioned it is very likely that the dimension legal status
(12) is mentioned also in a similar way (most or least important). The same goes (to a lesser extent)
for the combinations ‘range of subjects’ – ‘size’; ‘life long learning’ – ‘mode of delivery’; ‘international
research orientation’ – ‘regional engagement’ and ‘public character’ – ‘cultural engagement’.
For the other pairs listed it is likely that if one is mentioned important (innovation intensiveness) the
other is considered to be least important (size).
The other way to probe for possible clusters of dimensions used the answers to the relevance
questions, posed for each dimension. Again we calculated the bivariate correlations between
the scores. A high correlation between two dimensions means that if the respondent scores one
dimension as highly relevant it is likely that he scores the other dimension this way as well.
10 Factor analysis is not an option, due to the measurement level (which is ordinal).
MAPPING DIVERSITY