This document summarizes a study on the relationship between schools' socioeconomic status (SES) and university academic performance. The study used data from 8,417 undergraduate students attending 183 schools who enrolled at an anonymous university between 2011-2013. It analyzed the impact of student background characteristics, school characteristics, and prior academic achievement on first-year academic performance. The results showed that students from lower SES schools performed marginally better than those from higher SES schools. Individual SES background had no impact on university performance, and school resourcing characteristics did not impact academic performance. The implications are that university admission could advantage students from low SES schools, and that resource allocation to schools may not be an effective policy tool for improving academic
2. The University of Western Australia
Acknowledgements
National Centre for Student Equity in Higher Education
for funding support and data provision
Useful comments on the paper from John Phillimore and
participants of the Honouring Paul Miller event,
November 2014
This paper is dedicated to the memory of Paul Miller,
who conceived the original research question but who
passed on before the project commenced
All mistakes remains those of the authors.
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3. The University of Western Australia
Motivation for the research
Higher education reform
Bradley review 2008
Target of 40% of Australians aged 25-34 with degree by
2025
Equity target of 20% of higher education enrolments
from low SES backgrounds
Move to demand driven system in 2012
17.4% low SES enrolments in 2013 (Department of
Education 2014)
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4. The University of Western Australia
Data
De-identified student record data from an anonymous university
• Commencing undergraduate degree in 2011 to 2013
• Admitted on basis of completing Year 12
• Information on school where student graduate from
• 8,417 observations
Linked to the ABS Socio-economic Index for Areas
Linked to data from MySchool (ACARA)
• Contains data on schools’ characteristics
• 183 schools in the sample
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Student records
• Age
• Gender
• English-speaking
background
• Residential postcode
– Index of Economic
Resources
– Index of Education and
Occupation
• Primary field of study
• ATAR
• WAM in first year
MySchool
• Sector (Catholic,
Independent, Government)
• Rural/urban
• Co-ed, boys or girls school
• Funding per student (all
sources)
• Teacher/student ratio
• Non-teaching staff/student
ratio
• Index of Community
Socioeconomic Advantage
(ICSEA)
6. The University of Western Australia
ICSEA – A measure of Schools’ SES
Measure of students’ socio-educational similarity
Student level measures
• Parental education
• Parental occupation
• Geographical remoteness
School level measures
• Indigenous student enrolment
• NESB student enrolment
• Aggregated socio-educational measures
National mean of 1,000
• Advantaged if above 1,000, disadvantaged if below 1,000
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7. The University of Western Australia
Selected descriptive statistics
Mean ATAR = 82.3, mean WAM = 63.7 (8,417 obs)
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Variable Govt Cath Indp
ATAR 81.7 82.6 82.7
Weighted Average Mark 64.3 63.1 63.3
ICSEA (school SES) 1,041 1,065 1,117
Income per student 14,602.8 14,880.0 18,360.3
Teacher/student ratio 0.076 0.075 0.084
Non-teaching/student ratio 0.026 0.033 0.044
No. of schools 94 34 55
No. of students 3,478 2,580 2,359
8. The University of Western Australia
Methodology
Education production function
𝐴𝑃𝑖 = 𝑓 𝐵𝐶𝑖, 𝑆𝑖, 𝑃𝐴𝐴𝑖 , i = 1,…,n (1)
Where AP = academic performance
BC = background characteristics
S = school characteristics
PAA = prior academic achievement
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9. The University of Western Australia
Methodology – multilevel models
Hierarchical structure – students clustered within schools
Random intercept
𝐴𝑃𝑖 = 𝛼0𝑗 + 𝛼1 𝐵𝐶𝑖 + 𝛼2 𝑃𝐴𝐴 + 𝜀𝑖 (2)
𝑖 = 1, … 𝑛.
j = 1,…,k.
Random coefficients
𝐴𝑃𝑖 = 𝛼0 + 𝛼1𝑗 𝐵𝐶𝑖 + 𝛼2𝑗 𝑃𝐴𝐴 + 𝜀𝑖 (3)
𝛼1𝑗 = 𝑓 𝑆𝑖
𝛼2𝑗 = 𝑓 𝑆𝑖
𝑖 = 1, … 𝑛.
j = 1,…,k.
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10. The University of Western Australia
Standardisation of variables
For ease of interpretation, continuous variables of interest were
standardised
Standardisation has been done using population or grand means
• Comparison of between school effects
Standardisation (for ATAR and student SES) has also been done
using within school means in two models estimated (presented last)
• Comparison of within school effects
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11. The University of Western Australia
Is Schools’ SES associated with
WAM?
Is the impact of Schools’ SES on
WAM associated/affected by other
variables?
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Results
12. The University of Western Australia 12
Random intercept model results
Variable Model 1 Model 2
Age (at commencement) 0.408*** 0.392***
(0.081) (0.087)
Female 5.206*** 4.821***
(0.323) (0.326)
Foreign born 0.193 0.348
(0.417) (0.361)
NESB -0.323 -0.536
(0.578) (0.582)
IER+ 0.401** 0.414***
(0.174) (0.153)
IEO+ -0.140 -0.120
(0.205) (0.200)
ICSEA+ -0.637*** -0.729***
(0.238) (0.236)
FoS Not included Included
Prob > χ2 0.000 0.000
13. The University of Western Australia 13
Random intercept model results
Variable Model 3 Model 4
Independent school 0.679 0.909
(0.634) (0.637)
Catholic school 0.098 -0.084
(0.568) (0.602)
Rural school 0.478 0.796
(0.596) (0.609)
Boy’s school -2.824*** -2.127**
(0.940) (1.064)
Girl’s school -1.607*** -1.106*
(0.555) (0.668)
School income per student+ -1.267**
(0.560)
Teaching staff per student+ 0.694*
(32.473)
Non-teaching staff per student+ -0.095
(24.945)
ICSEA+ -0.611** -0.426
(0.308) (0.310)
Demographics Included Included
FoS Included Included
14. The University of Western Australia
How does prior academic achievement
impact on university academic
performance?
How does prior academic achievement
impact on the relationship between
schools’ SES and university performance?
Do certain schools provide better
platforms for university study?
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Results
15. The University of Western Australia 15
Random intercept model results
Variable Model 7 Variable Model 7
IER+ 0.202 Girl’s school -1.823***
(0.148) (0.703)
IEO+ -0.075 School income
per student+
-1.166**
(0.168) (0.491)
Independent
school
0.850 Teaching staff
per student+
16.649
(0.606) (32.030)
Catholic school -0.703 Non-teaching
staff per
student+
38.681
(0.532) (26.093)
Rural school 0.624 ATAR+ 5.944***
(0.599) (0.247)
Boy’s school -2.048** ICSEA+ -1.506***
(0.800) (0.277)
Demographics Included FoS Included
16. The University of Western Australia
Are there differences in the way within-
school variation in student characteristics
impact on the determinants of university
performance, particularly the role of
ATAR?
The following models standardise IER,
IEO and ATAR using means within
schools.
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Results
17. The University of Western Australia 17
Random intercept model results
^standardised using means within schools
Variables Model 8 Variables Model 8
IER^ -0.042 Girl’s school -1.078*
(0.136) (0.641)
IEO^ 0.135 School income per student+ -1.148**
(0.128) (0.561)
Independent school 0.804 Teaching staff per student+ 40.673
(0.653) (33.140)
Catholic school -0.212 Non-teaching staff per
student+
2.617
(0.613) (21.834)
Rural school 0.987 ATAR^ 5.870***
(0.609) (0.171)
Boy’s school -2.598** ICSEA+ -0.370
(1.117) (0.313)
Demographics Included FoS Included
18. The University of Western Australia
Are there differences in the way schools
translate prior academic ability into
university performance?
Are there differences in the way schools
with varying SES prepare their students
for university?
Use of random coefficient model
Slope of ATAR and ICSEA allowed to vary
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Results
19. The University of Western Australia 19
^standardised using means within schools
Random coefficient model results
Variables Model 9 Variables Model 9
IER^ -0.041 Girl’s school -1.381**
(0.135) (0.661)
IEO^ 0.141 School income per
student+
-1.166**
(0.129) (0.525)
Independent school 0.791 Teaching staff per
student+
44.400
(0.643) (32.059)
Catholic school -0.035 Non-teaching staff per
student+
5.591
(0.596) (21.808)
Rural school 0.675 ATAR^ 5.693***
(0.609) (0.176)
Boy’s school -2.635** ICSEA+ -0.386
(1.090) (0.309)
Demographics Included FoS Included
20. The University of Western Australia
Limitations
• Sample bias – students who have successfully gained
entry to university, despite SES background and/or
ATAR
• While the data covers 183 schools, only performance
at one university is examined
Key findings
• Students from lower SES schools perform marginally
better than peers from higher SES schools
• Individual SES background has no impact on
university performance
• School resourcing characteristics does not impact on
university performance
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Concluding remarks
21. The University of Western Australia
Implications
• Admission regimes at university could take into
account relatively good performance of students
from low SES schools and advantage them in
gaining entry
• Resource allocation – is it a useful policy tool for
improving academic performance? Findings of the
present study suggest not – consistent with other
studies (Marks 2010)
• Suggestions that resource quality rather than
quantity matters
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Concluding remarks
Editor's Notes
+ indicates a standardised variable.
Model 1 contains controls for only demographics. Model 2 includes FoS in addition. FoS effects are mainly significant and substantial but are not discussed here as not focus of presentation.
In both models ICSEA is statistically significant, and negative in sign. So students from disadvantaged schools actually fare better. Better access to finances (IER) does lead to a small positive effect though, of about half a mark when moving one standard deviation across the distribution of students. IEO has no statistically significant effect.
Females do very well, scoring about 5 percentage points better than males.
Model 3 includes controls for school characteristics, while model 4 includes controls for school resourcing.
General remark: findings for demographics and FoS earlier do not change.
School type does not appear to influence marks – all statistically insignificant and small in size.
School income has a small, negative effect. Teacher/student ratio has a small positive effect in model 4.
This model included same controls as model 4, but with addition of a control for ATAR.
Some general discussion first:
Individual level SES controls are statistically insignificant
School type does not influence marks
School sex does, students from both types of single sex schools perform poorer compared to co-ed schools
Result on school income consistent with previous models – small, negative effect.
Main finding: ATAR is an important determinant of academic performance – around 6 percentage points per standard deviation shift
After intro of ATAR as a control, effect of ICSEA has doubled from previous models – a moderate effect negative in sign – students from priviledged backgrounds don’t do as well, and have a reduction of 1.5 marks per std dev shift
In model 8, the variables ATAR, IER and IEO are standardised according to the means within schools.
Generally, the results in model 8 are consistent with previous models – ie standardising the variables using within school means as opposed to population means did not lead to different findings. Thus, no intra-school SES or prior ability effect is present.
Model 9 used a random coefficient model, where the slope coefficients of ATAR and ICSEA were allowed to vary. Nevertheless, the results in model 9 are qualitively identical to results in earlier models. Hence, there are no differences between schools in how prior ability is converted into subsequent academic performance at uni. Disadvantaged schools did not appear to have any differences when compared to privileged schools in terms of their students’ university performance.