Socioeconomic background and school type differences in University entrance in Australia: the role o

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Socioeconomic background and school type
differencesin University entrance in
Australiathe role of the stratified
curriculumGary N. MarksMarch 20 2009
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Overview of presentation

• Motivation
• Arguments and counter arguments on role of the
  stratified curriculum and
• School sector differences
• Socioeconomic differences (cultural capital)
• The two research questions
• Data
• Exogenous measures
• SES, school sector, ability
• Measure of subject clusters
• Outcome measures
• University entrance performance and university
  participation
• Methods
• Results of analysis of ENTER scores
• Results of analysis of university participation
• Conclusions

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Motivation

• Senior school curriculum
• Stratified
• Socially selective
• Important bearing on educational outcomes
• Argued
• Accounts for school sector differences
• Contributes to the reproduction of socioeconomic
  inequality

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School sector differences in university entrance 1

• Argument that non-government schools
• Influence education departments and universities
  on the relative weighting of subjects/courses for
  university entrance.
• Encourage their students to take these subjects.
• Devote considerable resources to these subjects
  to ensure that their students perform well.
• gt Non-government school students are more
  successful than other students in university
  entrance.
• Helps maintain the status of non-government
  schools as delivering better student outcomes to
  their cliental. i.e. market position.

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School sector differences in university entrance 2

• Counter Argument
• No conspiracy between education departments,
  universities and non-government (or independent)
  schools.
• Argument doesnt really apply to Catholic schools
  or low fee non-government schools.
• Not all jurisdictions weight subjects.
• Where weighting is used this is because some
  subjects are more difficult than others and
  attract higher ability students.
• Students in non-government schools perform better
  for other reasons.
• Government schools also offer university entrance
  subjects and vice-versa.
• Universities should be able to decide their
  selection criteria.

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Cultural capital and the reproduction of
socioeconomic inequality 1

• Cultural capital is associated with higher
  socioeconomic backgrounds.
• Since the curriculum is designed in the cultural
  context of the dominant culture, students
  familiar with the dominant culture (with higher
  levels of cultural capital) are advantaged.
• This is especially true for academic subjects
  which are weighted favourably for university
  entrance.
• Therefore students from higher socioeconomic
  backgrounds perform better.
• This contributes to the reproduction of
  socioeconomic inequality.

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Cultural capital and the reproduction of
socioeconomic inequality 2

• Counter Argument
• Relationship between students cultural capital
  and socioeconomic background is not strong.
• Intergenerational reproduction of socioeconomic
  background in Australia is only moderate and
  declining.
• Students with higher levels of cultural capital
  do perform better but some research suggests that
  this could be attributed to reading behaviour.
• Cultural context may be important for English and
  the humanities but less relevant for mathematics
  and the sciences.
• Immigrant students who are not familiar with the
  dominant culture perform well in the competition
  for university entrance.

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Two related research questions

• The extent that sector differences in university
  entry can be attributed and curriculum
  stratification during senior secondary school net
  of the socioeconomic and academic mix of students
• The extent that the effects of socioeconomic
  background are mediated by school sector and
  curriculum stratification.

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Data

• Longitudinal Surveys of Australian Youth 1995
  Year 9 Cohort
• 1995 to 2000 data waves
• Original Sample of 13,613
• Data weighted for student school non-response,
  differences between sample and population on
  certain variables PLUS attrition weights.

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Exogenous measures

• Socioeconomic background is a composite of
  parents occupation, parents education and
  wealth.
• School sector from sampling frame
• Ability measured by performance in Literacy and
  Numeracy in Year 9
• Measures of ability and SES standardized to a
  mean of zero and a standard deviation of one to
  facilitate comparison of effects.

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Subject clusters

• Mathematics, Physics Chemistry Advanced
  Mathematics and Science
• Technology Subjects Vocational subjects in
  Engineering, Technology
• Vocational White Collar- Business and Office
  Studies, and Childcare
• Legal, Business, Economics
• Geography, Biology, Psychology, Home Economics
• Performing Arts
• Languages, History, Literature
• Mixed but mostly intermediate Mathematics
  Science
• General Mixed No Predominant field of Study

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Outcome measures

• Tertiary entrance performance by ENTER score
• University participation

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Methods

• For analyses of ENTER score
• Sequential OLS regression models
• For analyses of university participation
• Sequential logistic regression models
• For both groups of analyses
• Standard errors adjusted for two-stage sample
  design, allow for clustering of students within
  schools.
• R square or pseudo R square for measure of fit.
• Analyses weighted for differences between origin
  sample and population and for attrition.

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Students tertiary entrance performance 1

• School sector explains little variation in ENTER
  (model 2)
• 10 point difference for independent vs.. Gov.
  Schools
• 5 point difference for catholic vs.. Government
  schools
• Little variation explained (4 )
• SES explains over 7 of the variation (model 1)
• A 1 SD difference in SES 5 ENTER points
• SES sector explains over 10 of the variation
  (model 3)
• Effect of SES decreases marginally
• Effect for independent school declines from 10 to
  6 score points
• Adding ability (model 4) increases rsq from 10 to
  27
• A 1 SD difference in ability 10 ENTER points
• Effect of SES declines to 3 score points
• Effect of independent school 5 score points net
  of SES ability
• Effect of a catholic school 3 score points net of
  SES ability

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Students tertiary entrance performance 2

• Model 5 includes subject cluster
• Strong effect for
• Maths, physics, chem. Cluster 9 score points
• Mixed but mainly maths 5.5 score points
• Languages 4.5 score points
• Negative effect for vocational subjects -7 score
  points
• Others, not statistically significant
• These effects net of school sector, SES and
  ability.
• gt Subject cluster has strong independent effects
  on enter score (i.e.. Net of sector, SES and
  ability).
• In model 6, ability is dropped and the effects
  for subject cluster increase in magnitude by
  approximately 40 so ability accounts for a
  substantial part of the effect of subject
  cluster.

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Students tertiary entrance performance 3

• Model 5 includes subject cluster
• Note for Model 5
• R square increases from 27 to 33 per cent
• Effect of independent school declines from 5.5 to
  5.1 score points, so effect of independent school
  only partially mediated by subject cluster
• Effect of Catholic school declines from 5.1 to
  4.7 score points, so effect of independent school
  only partially mediated by subject cluster
• Effect of SES declines from 3.1 to 2.7 score
  points, so effect of SES only partially mediated
  by subject cluster
• Effect of Ability declines from 10 to 8.6 score
  points, so effect of ability is not mediated by
  subject cluster

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University entry 1

• Estimates are logistic regression coefficients
• To interpret estimates, take exponent of
  coefficients for odds ratios.
• Example 1. Coefficient 0.6 means the odds of
  catholic school students participating at
  university are 1.8 times that for government
  school students. Exp(0.6) 1.8
• Example 2. Coefficient 0.5 for SES means the odds
  of participating at university increase 1.6 times
  for a one standard deviation in SES. Exp(0.5)
  1.6
• For a 4 standard deviation in SES (comparing the
  extremes of the SES distribution), odds ratio of
  7.4.
• In model 4 a one standard deviation difference in
  ability translated to an odds ratio of 2.2 times.
  A 4 SD difference translates to an odds ratio of
  22.6.

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University entry 2

• Overall patterns are very similar to that for
  ENTER scores
• Only moderate effects for SES. Even smaller if
  control for student achievement.
• SES only partially explains school sector
  differences
• Ability explains more of independent-government
  school gap but little of differences between
  Catholic government school students.
• Variation explained increases substantially with
  the addition of ability (from 9 to 20 ).

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University entry 3

• Most subject clusters have significant effects on
  university participation
• Strong ve effects for Maths/Science and mixed
  maths
• Weaker ve effects for languages, history and
  literature
• Still weaker ve effects for geography, biology
  etc.
• -ve effects for technology and vocational
• Note these effects for subject cluster are net of
  SES and ability.
• BUT subject clusters do NOT account for school
  sector differences or the effect of SES
• With the addition of subject cluster (model 5 on
  model 5)
• Effect for Catholic school (vs. gov. school)
  declines from 0.50 to 0.48.
• Effect for Independent school (vs. gov. school)
  declines from 0.57 to 0.52.
• Effect for SES declines from 0.33 to 0.27.
• Effect for ability declines from 0.78 to 0.54

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Conclusions

• Course type during senior school year has a
  strong impact on both tertiary entrance
  performance and university participation.
• But course type only very partially mediates for
  the effects of socioeconomic background and
  school sector.
• gt that curriculum stratification is NOT a major
  factor contributing to the stronger academic
  outcomes of students at independent schools or
  more broadly, socioeconomic inequalities in
  education.