The impact of schools on young people's transition to university

Comparing influential school attributes against performance
So far the analysis has identified influential school attributes and assessed them in terms <strong>of</strong> their
importance for TER and the probability <strong>of</strong> university enrolment by age 19. This final section <strong>of</strong> the
analysis focuses on the highest and lowest performing <strong>schools</strong> to explore what distinctive characteristics
each <strong>of</strong> these sets <strong>of</strong> school has. Two steps are taken to answer this question. <strong>The</strong> first step consists <strong>of</strong>
undertaking a cluster analysis, 24 which groups <strong>schools</strong> by their performance across both outcomes. Here,
school performance is predicted from the multi-level model, assuming that each student has the
characteristics <strong>of</strong> an ‘average’ student. <strong>The</strong> second step consists <strong>of</strong> identifying the actual characteristics
<strong>of</strong> <strong>schools</strong> across performance clusters. Results from cluster analysis are shown in figure 8.
Figure 8 Cluster analysis <strong>of</strong> school performance
24 Cluster analysis is an exploratory data analysis method used to group similar entities across a range <strong>of</strong> variables. For
more details on cluster analysis, readers are referred to Everitt et al. (2011). Detailed results <strong>of</strong> the cluster analysis
are not presented due to space restrictions, but are available from the authors upon request.
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