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

Recommendations

Info

Figure 7 Caterpillar plots <strong>of</strong> between-school differences for university enrolment after multi-level modelling Note: <strong>The</strong> analysis <strong>of</strong> university enrolment accounts for students’ TER using an indicator variable. Students with a valid TER are grouped by quartile. An additional category contains students for whom a valid TER is not reported. <strong>The</strong> background characteristics <strong>of</strong> individuals are held constant at their mean values. All individual plots for government, Catholic, and independent <strong>schools</strong> reference the grand mean across all <strong>schools</strong> in the sample. 28 <strong>The</strong> <strong>impact</strong> <strong>of</strong> <strong>schools</strong> 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. NCVER 29
Page 1: LONGITUDINAL SURVEYS OF AUSTRALIAN
Page 4 and 5: © Commonwealth of
Page 7 and 8: Contents Tables and figures 6 Execu
Page 9 and 10: 8 Cluster analysis of</stro
Page 11 and 12: can be identified statistically but
Page 13 and 14: Current knowledge about school effe
Page 15 and 16: Analysis Data and sample This study
Page 17 and 18: Academic orientation Academic orien
Page 19 and 20: Results This section first presents
Page 21 and 22: School attribute Categories ß SE J
Page 23 and 24: variance which can be attributed to
Page 25 and 26: School attribute Categories ß SE O
Page 27 and 28: student—teacher ratio, competitiv
Page 29: Figure 6 Caterpillar plots
Page 33 and 34: in over two-thirds of</stro
Page 35 and 36: Table 8 Geographic distribution <st
Page 37 and 38: Figure 10 Components of</st
Page 39 and 40: References Australian Curriculum, A
Page 41 and 42: Appendix A: Descriptive statistics
Page 43 and 44: School attribute Categories TER (N
Page 45 and 46: Perceptions of the
Page 47 and 48: Figure C1 Scree plot from SES facto
Page 49 and 50: Figure D1 Scree plot from perceptio
Page 51 and 52: In this paper, the ICC for the cont
Page 53 and 54: Table F2 Results for student-level
Page 55 and 56: School attribute Categories df Join
Page 57 and 58: Table F5 Results for school-level p
Page 59: Appendix G: Information on the Logi

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?