The impact of schools 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