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

School attribute Categories ß SE Odds
ratio 19
School autonomy
Responsibility for resources
Responsibility for the
curriculum
Influence <strong>of</strong> business on
the curriculum
Providing for student needs
Level <strong>of</strong> extracurricular
activities
Teacher vs counsellorbased
career advice
Perceptions <strong>of</strong> the school
experience at the school
level
Competition
Two or more
<strong>schools</strong>
One other
school
No other
school
Not significant
Not significant
Not significant
Not significant
Not significant
Not significant
Joint df Within-category
Wald χ 2 comparisons
Reference category 6.53 2 No sig. diff.
between Against
0.34 0.22 1.40
one other school
and Against no
other school
0.57 0.27 1.76
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> 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> continuous predictor SES is grand-mean centred and standardised to a mean <strong>of</strong> 0 and a standard deviation <strong>of</strong> 1.
<strong>The</strong> analysis <strong>of</strong> university enrolment accounts for students’ TER using an indicator variable. Students with a valid TER are
grouped by quartile. An additional category contains students for whom a valid TER is not reported.
According to the findings from the analysis, factors that are significant in affecting the probability <strong>of</strong>
going on to university are:
School sector: students attending government <strong>schools</strong> are less likely to transition to university by
age 19.
Average SES <strong>of</strong> the school: while this variable was not significant in the TER model, it does affect
the probability <strong>of</strong> transitioning to university by age 19. Students who attend lower-socioeconomic
status <strong>schools</strong> have significantly lower odds <strong>of</strong> going to university by age 19, even after controlling
for individual TER. This finding is consistent with prior studies (see NOUS Group et al. 2011; OECD
2010).
Language backgrounds other than English: young people who attend <strong>schools</strong> with the highest
concentration <strong>of</strong> students with a language background other than English are more likely to
transition to university by age 19. Again, readers are cautioned that these students vary greatly in
terms <strong>of</strong> their circumstances, ranging from refugee students, to those whose parents have come to
Australia as highly educated and highly skilled immigrants. Thus, blanket statements about a
‘general’ positive effect from higher proportions <strong>of</strong> these students need to be qualified.
Competitive position: similar to TER, the analysis shows that <strong>schools</strong> which deviate from the norm
show better outcomes with respect to transitioning to university by age 19. Students attending
<strong>schools</strong> in the category <strong>of</strong> ‘competing against no other school’ have significantly higher odds <strong>of</strong>
going on to university. However, readers are reminded that the competition measure lacks
variation and therefore should be interpreted with caution.
NCVER 23