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

108 Discussion
In this chapter we discuss the results of the survey and the consequences these may have for the
selection and clustering of dimensions and indicators. First we explore the dimensions with ‘poor’
indicators. The survey showed that a number of indicators scored signifi cantly lower on criteria such
as validity and feasibility than others. Are these poor indicators evenly dispersed over the dimensions
or can we identify particularly ‘challenging’’ dimensions (dimensions with predominantly poor
indicators)? And what consequences may that have for the selection of dimensions and indicators?
The focus then turns to a way to cluster the dimensions, using the scores on the questions on
relevance.
‘Challenging’ dimensions
One of the reasons for organising the survey was to fi nd out which dimensions and indicators would
work and which would not. To fi nd an answer to this question we combined the information on the
validity, feasibility and reliability of the indicators selected for each dimension. We do not use the
scores on the perceived relevance of the dimensions since a high proportion of responding higher
education institutions strongly disagreeing with the relevance of a dimension is not an indication of
the quality of the dimension. We see such a lack of consensus as an indication of the diversity of the
missions and profi les of the higher education institutions. Only if the vast majority of the responding
higher education institutions disagreed with the relevance would we reconsider the choice of this
dimension. This was not the case with any of the fourteen dimensions.
To identify potential challenging dimensions we selected those dimensions for which at least one
indicator scores more than 5% strongly disagree on the validity and reliability items and is in the
bottom fi ve of the overall feasibility ranking.
Using these criteria, there are two ‘challenging’ dimensions: dimension 4, ‘Involvement in live long
learning’ and dimension 6 ‘innovation intensiveness’.
If we use the validity and feasibility criteria only one more dimension emerges as being ‘challenging’:
‘regional engagement’.
If we use the validity and reliability criteria there are four ‘challenging’ dimensions: ‘program orientation’,
‘involvement in life long learning’, ‘research intensiveness’, and ‘innovation intensiveness’.
MAPPING DIVERSITY
If we use the feasibility and reliability criteria again only the dimensions ‘Involvement in life long
learning’ and ‘innovation intensiveness’ emerge.
Clustering dimensions
The scores on the relevance questions can also be used to cluster the dimensions. First we look
at the scores on the question where the three most and the three least important dimensions were
identifi ed. If the scores on two dimensions correlate positively it is likely (Teeuwen 2004) that
whenever a respondent thinks one of the indicators is most important he thinks the other indicator
is most important as well. If a pair of dimensions are negatively correlated, it is likely that whenever
a respondent thinks one of the indicators as most important he thinks the other indicators is least
important. There are fi ve pairs of dimensions that appear to correlate positively and seven that
correlate negatively.