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

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interactions between gender and other predictor variables were also investigated. 14 No significant interactions were found, which meant there was no need to examine males and females separately. While individual background characteristics are important, the purpose <strong>of</strong> this study is to identify school attributes that significantly <strong>impact</strong> on outcomes after accounting for relevant individual student characteristics. Table 2 summarises results for school-level predictors <strong>of</strong> TER. <strong>The</strong> effect <strong>of</strong> any given predictor in the table is expressed through its ß-coefficient, which captures the increase/ decrease in TER for each one-unit change in that predictor’s unit <strong>of</strong> measurement. Complete results from multi-level analysis for all student and school-level predictors <strong>of</strong> TER are provided in appendix F. Table 2 Results for school-level predictors <strong>of</strong> TER School attribute Categories ß SE Joint df Within-category Wald χ 2 comparisons School sector Government Reference category 10.28 2 No sig. diff. Catholic 2.50 0.98 between Catholic and independent Independent 2.47 0.87 School location School demographics Not significant Size Continuous 0.30 0.10 13.80 1 SES Academic achievement Not significant Not significant Gender mix Coed Reference category 7.54 2 No sig. diff. All-male 3.18 1.34 between All male and All female All-female 1.85 1.18 LBOTE quartile No LBOTE Reference category 11.54 4 Sig. diff. between Q1 (lowest) 2.98 1.05 LBOTE Q1 and LBOTE Q3 Q2 1.30 0.99 Resources and capacity Class size Q3 0.38 1.08 Q4 (highest) 2.89 1.15 Not significant Student—teacher ratio Continuous -0.45 0.11 18.16 1 Degree <strong>of</strong> teacher shortage Prop. certified teachers Prop. highly qualified teachers Primary resource base Quality <strong>of</strong> educ. materials Academic orientation Not significant Not significant Not significant Not significant Not significant Acad. pressure from parents Weak Reference category 11.09 1 Student selection criteria Strong 2.33 0.70 Not significant Use <strong>of</strong> streaming For some subjects Reference category 7.57 2 No sig. diff. For all subjects 2.29 1.48 between For all subjects and For For no subjects 2.98 1.24 no subjects Exposure to work Continued next page More than half <strong>of</strong> students participate Half or less <strong>of</strong> students participate Not <strong>of</strong>fered 3.43 1.15 Reference category 21.10 2 No sig. diff. between Half or 4.82 1.06 less <strong>of</strong> students participate and Not <strong>of</strong>fered 14 Two predictors are said to have an interaction effect on the outcome <strong>of</strong> interest if the effect <strong>of</strong> one predictor depends on the value <strong>of</strong> the other predictor. 18 <strong>The</strong> <strong>impact</strong> <strong>of</strong> <strong>schools</strong> on young people’s transition to university
School attribute Categories ß SE Joint df Within-category Wald χ 2 comparisons School autonomy Resp. for resources Resp. for the curriculum Influence <strong>of</strong> business on the 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 Two or more <strong>schools</strong> Not significant Not significant Not significant Not significant Not significant Not significant One other school 3.37 1.91 No other school 4.58 1.62 -2 Log-Likelihood Null model 32897 Final model 27921 Reference category 10.35 2 No sig. diff. between Against one other school and Against no other school Note: All statistical tests are based on a significance level <strong>of</strong> α = .05. All categorical predictors listed above have joint statistical significance, thus all Wald χ 2 values are bolded. Within each categorical predictor, categories that are statistically significantly different from the reference category have bolded ß-coefficients and standard errors. <strong>The</strong> continuous predictors Size and Student-teacher ratio are grand-mean centred. <strong>The</strong> ß-coefficient and standard error for Size is per each 100-student change from the mean school size <strong>of</strong> 888 students in the sample. <strong>The</strong> model includes a random intercept for each school, and random slopes (for each school) for the individual variables <strong>of</strong> Gender and SES. <strong>The</strong> latter means that some <strong>schools</strong> are better for females, for example, or students <strong>of</strong> a particular socioeconomic background. LBOTE = language background other than English. <strong>The</strong> major points to emerge are: <strong>The</strong> average TER <strong>of</strong> the <strong>schools</strong> in the Catholic and independent sectors is around 2.5 points higher than <strong>schools</strong> in the government sector. This is in line with previous studies (see Curtis & McMillan 2008; Marks 2007, 2010a). Academic orientation is important. Those <strong>schools</strong> with strong pressure from parents to achieve academic success and those in which few students undertake work experience have higher average TERs. For reasons the data cannot uncover, <strong>schools</strong> which deviate from the norm do better when it comes to TER. <strong>The</strong>se inherently different <strong>schools</strong> represent a small minority in the sample and include those that are single-sex (consistent with earlier research such as Gill 2004), those that do not compete with other <strong>schools</strong> and those that either do not stream at all or stream for every subject. Size and resources have some <strong>impact</strong>, with larger <strong>schools</strong> and those with lower student—teacher ratios doing better. One reason that works in favour <strong>of</strong> larger <strong>schools</strong> is that small <strong>schools</strong> are <strong>of</strong>ten faced with higher average costs due to low enrolments (NOUS Group et al. 2011). Higher average costs make it more difficult for smaller <strong>schools</strong> to afford more teachers and reduce class sizes, which may, in turn, affect academic achievement and TER. <strong>The</strong> proportion <strong>of</strong> students from non-English speaking backgrounds matters, in that those <strong>schools</strong> with a high percentage <strong>of</strong> students with a language background other than English deliver better NCVER 19
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: Results This section first presents
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 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

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