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

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Table F3 Results for school-level predictors <strong>of</strong> TER School attribute Categories df Joint Wald χ 2 Model 3 (Final model, n = 329) Model 1 Model 2 Wald χ 2 Coeff. SE School sector Government [R] 2 10.11 10.35 10.28 Catholic 2.501 0.976 Independent 2.472 0.869 School location Not metropolitan [R] 1 1.19 d School demographics Metropolitan Size Continuous 1 12.04 13.49 13.80 0.003 0.001 SES Continuous 1 0.22 d Acad. achievement Continuous 1 3.66 d Gender mix Co-ed [R] 2 5.65 9.53 7.54 All male 3.176 1.342 All female 1.852 1.18 LBOTE quartile No LBOTE students 4 11.77 8.65 11.54 Resources and capacity LBOTE Q1 (lowest) 2.977 1.046 LBOTE Q2 1.301 0.993 LBOTE Q3 0.384 1.078 LBOTE Q4 (highest) 2.886 1.154 Class size Large (> 20) [R] 1 1.15 d Student–teacher ratio Degree <strong>of</strong> teacher shortage Prop. certified teachers Prop. highly qual. teachers Primary resource base Quality <strong>of</strong> educ. materials Academic orientation Acad. press. from parents Student selection criteria Small (≤ 20) Continuous 1 9.98 16.20 18.16 -0.447 0.105 Continuous 1 2.79 d Continuous 1 2.55 d Continuous 1 2.78 d Government [R] 1 0.20 d Non-government Continuous 1 0.02 d Weak [R] 1 5.96 10.65 11.09 Strong 2.328 0.699 At least one prereq. [R] Considered Not considered Use <strong>of</strong> streaming For some subjects [R] Exposure to work Continued next page 2 1.08 d 2 12.35 7.02 7.57 For no subjects 2.984 1.24 For all subjects 2.292 1.48 For more than half <strong>of</strong> students [R] For half or less <strong>of</strong> students 2 15.53 25.68 21.10 4.817 1.058 Not <strong>of</strong>fered 3.426 1.153 52 <strong>The</strong> <strong>impact</strong> <strong>of</strong> <strong>schools</strong> on young people’s transition to university
School attribute Categories df Joint Wald χ 2 Model 3 (Final model, n = 329) School autonomy Resp. for resources Resp. for curriculum Business influence on curr. Providing for student needs Extracurricular activities Continuous 1 1.38 d Continuous 1 0.29 d No influence [R] 2 0.89 d Minor influence Considerable influence Career guidance Counsellor-based [R] Perceptions <strong>of</strong> schooling Competition Continuous 1 3.89 0.62 Teacher-based Model 1 Model 2 Wald χ 2 Coeff. SE 1 2.29 d Continuous 1 0.61 d Two or more <strong>schools</strong> [R] 2 9.68 13.05 10.35 One other school 3.374 1.909 No other school 4.579 1.622 -2 Log-Likelihood Null model 32897 Final model 27921 NCVER 53
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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 and 30: Figure 6 Caterpillar plots
Page 31 and 32: Comparing influential school attrib
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: Table F2 Results for student-level
Page 57 and 58: Table F5 Results for school-level p
Page 59: Appendix G: Information on the Logi
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