Schools' SES and University Academic Performance

Schools’ SES and University Academic Performance

13TH MARCH 2015, IAN LI AND MIKE DOCKERY

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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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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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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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)


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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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


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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Methodology – multilevel models

Hierarchical structure – students clustered within schools

Random intercept

𝐴𝑃𝑖 = 𝛼0𝑗 + 𝛼1𝐵𝐶𝑖 + 𝛼2𝑃𝐴𝐴 + 𝜀𝑖 (2)

𝑖 = 1, … 𝑛.

j = 1,…,k.

Random coefficients

𝐴𝑃𝑖 = 𝛼0 + 𝛼1𝑗𝐵𝐶𝑖 + 𝛼2𝑗𝑃𝐴𝐴 + 𝜀𝑖 (3)

𝛼1𝑗 = 𝑓 𝑆𝑖

𝛼2𝑗 = 𝑓 𝑆𝑖

𝑖 = 1, … 𝑛.

j = 1,…,k.

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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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Is Schools’ SES associated with

WAM?

Is the impact of Schools’ SES on

WAM associated/affected by other

variables?

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Results


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


Random intercept model resultsVariable 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


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



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


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



^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)



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


^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)



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


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