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

1. Schools’ SES and
University Academic
Performance
13TH MARCH 2015, IAN LI AND MIKE DOCKERY

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.
 2

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)
 3

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
 4

5. The University of Western Australia 5
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
 6

7. The University of Western Australia
Selected descriptive statistics
 Mean ATAR = 82.3, mean WAM = 63.7 (8,417 obs)
 7
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
 8

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.
 9

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
 10

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?
 11
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?
 14
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.
 16
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
 18
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
 20
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
 21
Concluding remarks