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

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Conclusion <strong>The</strong> purpose <strong>of</strong> this study was to determine whether, and to what extent, school characteristics can <strong>impact</strong> on TER and the probability <strong>of</strong> university enrolment by age 19. <strong>The</strong> analysis highlights the importance <strong>of</strong> a school’s characteristics. Although individual factors are the main drivers <strong>of</strong> success, school characteristics are responsible for almost 20% <strong>of</strong> the variation in TER and 9% <strong>of</strong> the variation in the probability <strong>of</strong> university enrolment, after controlling for TER. However, for TER only around seven percentage points <strong>of</strong> the variation can be attributed to the school characteristics that can be explained through the variables included in the analysis. <strong>The</strong> remaining 13 percentage points reflect features that are peculiar to certain <strong>schools</strong>, with these idiosyncratic (or school ‘ethos’) factors producing differences between <strong>schools</strong> that can be measured statistically but cannot be explained using the LSAY data. <strong>The</strong> most influential factors to emerge from the analysis are the role <strong>of</strong> sector, academic orientation, differentiation from the norm and resourcing. In relation to sector, high-performing <strong>schools</strong> include government, Catholic and independent <strong>schools</strong>. However, even after controlling for relevant characteristics, the low-performing <strong>schools</strong> are almost all government. Academic orientation, as measured through parental pressure for the school to perform academically is important, as are the limitations imposed by the timetable <strong>of</strong> work-related programs. Schools that deviate from the norm (single-sex <strong>schools</strong>, the small number <strong>of</strong> <strong>schools</strong> that do not see themselves as competing with other <strong>schools</strong>, and the few which either stream all or no subjects) perform better than average, as do those with high proportions <strong>of</strong> students from language backgrounds other than English. <strong>The</strong> analysis further shows that resources do have some <strong>impact</strong>. On average, <strong>schools</strong> with lower student—teacher ratios obtain slightly better TERs, and student fees contribute more to school funds among <strong>schools</strong> in the high-performing cluster. Another interesting insight this paper generates is with respect to the role <strong>of</strong> school-SES. Previous studies (Gonski et al. 2011; NOUS et al. 2011; OECD 2010; Perry & McConney 2010) find school-SES to affect academic achievement up to age 15. This study finds that a school’s overall socioeconomic status matters for university enrolment at age 19, but does not influence students’ TER outcomes at the end <strong>of</strong> senior secondary schooling, conditioning on academic achievement at age 15. However, school SES does <strong>impact</strong> on the probability <strong>of</strong> going on to university for a given TER. Other school attributes which influence (positively) this probability are a high proportion <strong>of</strong> non-English speaking background students, and being Catholic and Independent rather than Government. Overall, this analysis was about the important, albeit not exclusive, aim <strong>of</strong> schooling to prepare students for the transition to higher education. Results show that <strong>schools</strong> matter in relation to TER and the probability <strong>of</strong> going on to university over and above individual student characteristics. 36 <strong>The</strong> <strong>impact</strong> <strong>of</strong> <strong>schools</strong> on young people’s transition to university
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Page 1: LONGITUDINAL SURVEYS OF AUSTRALIAN
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Page 7 and 8: Contents Tables and figures 6 Execu
Page 9 and 10: 8 Cluster analysis of</stro
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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
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Page 25 and 26: School attribute Categories ß SE O
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Page 31 and 32: Comparing influential school attrib
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Page 35 and 36: Table 8 Geographic distribution <st
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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
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