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

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Table A2 Descriptive statistics for school-level predictors (unweighted) School attribute Categories TER (N = 341) University enrolment (N = 354) n % n % School sector Government 214 62.8 222 62.7 Catholic 72 21.1 73 20.6 Independent 55 16.1 59 16.7 Location Not metropolitan 112 32.8 121 34.2 School demographics Metropolitan 229 67.2 233 65.8 Size Continuous Mean: 888 SD: 397 Mean: 868 SD: 407 SES Continuous Mean: 0 SD: 1 Mean: 0 SD: 1 Acad. achievement Continuous Mean: 0 SD: 1 Mean: 0 SD: 1 Gender mix Co-ed 287 84.2 300 84.7 All male 25 7.3 25 7.1 All female 29 8.5 29 8.2 LBOTE quartile No LBOTE student 208 61.0 189 53.4 Resources and capacity LBOTE Q1 (lowest) 24 7.0 36 10.2 LBOTE Q2 34 10.0 44 12.4 LBOTE Q3 38 11.1 43 12.1 LBOTE Q4 (highest) 37 10.9 42 11.9 Class size Large (> 20) 310 90.9 315 89.0 Small (≤ 20) 26 7.6 34 9.6 Missing 5 1.5 5 1.4 Student-teacher ratio Continuous Mean: 13.5 SD: 2.6 Mean: 13.4 SD: 2.6 Degree <strong>of</strong> teacher shortage Continuous Mean: 0 SD: 1 Mean: 0 SD: 1 Prop. certified teachers Continuous Mean: 0.98 SD: 0.12 Mean: 0.98 SD: 0.13 Prop. highly qual. teachers Continuous Mean: 0.97 SD: 0.09 Mean: 0.97 SD: 0.09 Primary resource base Government 282 82.7 294 83.1 Non-government 46 13.5 46 13.0 Missing 13 3.8 14 4.0 Quality <strong>of</strong> educ. materials Continuous Mean: 0 SD: 1 Mean: 0 SD: 1 Academic orientation Acad. pressure from parents Weak 214 62.8 225 63.6 Student selection criteria Strong 125 36.7 127 35.9 Missing 2 0.6 2 0.6 At least one criterion prerequisite 11 3.2 11 3.1 Considered 237 69.5 243 68.6 Not considered 91 26.7 98 27.7 Missing 2 0.6 2 0.6 Use <strong>of</strong> streaming For some subjects 302 88.6 312 88.1 Exposure to work Continued next page For no subjects 17 5.0 19 5.4 For all subjects 19 5.6 20 5.6 Missing 3 0.9 3 0.8 For more than half <strong>of</strong> students For half or less <strong>of</strong> students 30 8.8 36 10.2 180 52.8 184 52.0 Not <strong>of</strong>fered 125 36.7 127 35.9 Missing 6 1.8 7 2.0 40 <strong>The</strong> <strong>impact</strong> <strong>of</strong> <strong>schools</strong> on young people’s transition to university
School attribute Categories TER (N = 341) School autonomy University enrolment (N = 354) n % n % Responsibility for resources Continuous Mean: 0 SD: 1 Mean: 0 SD: 1 Responsibility for curriculum Continuous Mean: 0 SD: 1 Mean: 0 SD: 1 Business influence on curriculum Providing for student needs Level <strong>of</strong> extracurricular activities Teacher vs counsellor-based career advice Perceptions <strong>of</strong> the school experience at the school level Competition No influence 54 15.8 55 15.5 Minor influence 254 74.5 262 74.0 Considerable influence 30 8.8 34 99.2 Missing 3 0.9 3 0.8 Continuous Mean: 0 SD: 1 Mean: 0 SD: 1 Counsellor-based 129 37.8 131 37.0 Teacher-based 195 57.2 205 57.9 Missing 17 5.0 18 5.1 Continuous Mean: 0 SD: 1 Mean: 0 SD: 1 Two or more <strong>schools</strong> 283 83.0 292 82.5 One other school 25 7.3 27 7.6 No other school 31 9.1 33 9.3 Missing 2 0.6 2 0.6 Notes: <strong>The</strong> continuous predictors School SES, School academic achievement, Teacher shortage, Quality <strong>of</strong> educational materials, Responsibility for resources, Responsibility for the curriculum, Extracurricular activities, and Perceptions <strong>of</strong> schooling are standardised to a mean <strong>of</strong> 0 and a standard deviation <strong>of</strong> 1. <strong>The</strong> continuous predictors School size and Student–teacher ratio report absolute numbers. <strong>The</strong> continuous predictors Proportion <strong>of</strong> LBOTE students, Proportion <strong>of</strong> properly certified students, and Proportion <strong>of</strong> highly qualified teachers report absolute proportions. Cases with missing data were excluded from the analysis. Even though multiple imputation is the recommended approach for handling missing data (see Gemici, Bednarz & Lim 2011), s<strong>of</strong>tware for multiple imputation procedures in multi-level models (as outlined in Goldstein et al. 2009) is currently still in an experimental stage. It was thus decided to not use such s<strong>of</strong>tware in this analysis. Table A3 Descriptive statistics for outcome variables (unweighted) Outcome variable Categories TER (N = 3796) University enrolment (N = 6315) n % n % TER Continuous Mean: 77.14 SD: 16.41 - - Ever commenced bachelor degree or higher by 2010 Yes - - 3039 48.1 No - - 3276 51.9 NCVER 41
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 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: Appendix A: Descriptive statistics
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

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