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NAPLAN Scores as Predictors of Access to Higher Education in Victoria

Author

Listed:
  • Brendan Houng

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • Moshe Justman

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne; and Department of Economics, Ben Gurion University)

Abstract

This paper examines the extent to which year-9 performance on the National Assessment Program—Language Arts and Numeracy (NAPLAN) predicts access to higher education as determined by subsequent achievement on year-12 Victoria Certificate of Education (VCE) exams. VCE performance is measured via three binary indicators: achieving an Australian tertiary admission rank (ATAR) above 50 ("ATAR50"), above 70 ("ATAR70"), and above 90 ("ATAR90"); and two continuous indicators: ATAR and the Tertiary Entrance Aggregate (TEA). We find that a four-way classification of year-9 NAPLAN results explains 35% of the variance in ATAR50, 37% in ATAR70 and 26% in ATAR90; and NAPLAN scores and basic demographic indicators explain 38% of the variance in ATAR and 42% of the variance in TEA values. Examining the joint effect of year-9 NAPLAN scores and socio-economic status in predicting VCE outcomes, we find that while both are significant, NAPLAN scores have a much stronger effect. At the school level, we find that predictions of success rates based on NAPLAN scores and basic demographic indicators explain over 82% of the variance in school achievement in each of the binary indicators.

Suggested Citation

  • Brendan Houng & Moshe Justman, 2014. "NAPLAN Scores as Predictors of Access to Higher Education in Victoria," Melbourne Institute Working Paper Series wp2014n22, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    • Handle: RePEc:iae:iaewps:wp2014n22
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    File URL: http://melbourneinstitute.unimelb.edu.au/downloads/working_paper_series/wp2014n22.pdf
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    Cited by:

    1. Justman, Moshe & Méndez, Susan J., 2018. "Gendered choices of STEM subjects for matriculation are not driven by prior differences in mathematical achievement," Economics of Education Review, Elsevier, vol. 64(C), pages 282-297.
    2. Ian W. Li & A. Michael Dockery, 2015. "Does School Socio-economic Status Influence University Outcomes?," Australian Journal of Labour Economics (AJLE), Bankwest Curtin Economics Centre (BCEC), Curtin Business School, vol. 18(1), pages 75-94.

    More about this item

    Keywords

    Standardized tests; NAPLAN; ATAR; longitudinal analysis; predicting educational achievement; socio-economic gradient of achievement; school effects; Victoria; Australia;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

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