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Student Outcomes At University In Australia: A Quantile Regression Approach

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  • ELISA ROSE BIRCH
  • PAUL W. MILLER
Abstract
Students’ success during their first year at university is largely influenced by their university entrance score. Personal characteristics and secondary school characteristics also impact on success. This paper uses quantile regression to investigate how the effects of these factors vary along the grade distribution. It finds that the factors which influence grades have a more pronounced impact on the success of low‐achieving students than on that of high‐achieving students. These results have implications for student selection and also for the way scholarships may be used to attract talented high school students.

Suggested Citation

  • Elisa Rose Birch & Paul W. Miller, 2006. "Student Outcomes At University In Australia: A Quantile Regression Approach," Australian Economic Papers, Wiley Blackwell, vol. 45(1), pages 1-17, March.
    • Handle: RePEc:bla:ausecp:v:45:y:2006:i:1:p:1-17
    DOI: 10.1111/j.1467-8454.2006.00274.x
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    References listed on IDEAS

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    1. Weili Ding & Steven F. Lehrer, 2007. "Do Peers Affect Student Achievement in China's Secondary Schools?," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 300-312, May.
    2. Fertig, Michael & Schmidt, Christoph M., 2002. "The Role of Background Factors for Reading Literacy: Straight National Scores in the PISA 2000 Study," IZA Discussion Papers 545, Institute of Labor Economics (IZA).
    3. Fertig, Michael, 2003. "Who's to Blame? The Determinants of German Students' Achievement in the PISA 2000 Study," RWI Discussion Papers 4, RWI - Leibniz-Institut für Wirtschaftsforschung.
    4. Nicole Schneeweis & Rudolf Winter-Ebmer, 2008. "Peer effects in Austrian schools," Studies in Empirical Economics, in: Christian Dustmann & Bernd Fitzenberger & Stephen Machin (ed.), The Economics of Education and Training, pages 133-155, Springer.
    5. Weili Ding & Steven F. Lehrer, 2007. "Do Peers Affect Student Achievement in China's Secondary Schools?," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 300-312, May.
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    Cited by:

    1. Buly A. Cardak & Joe Vecci, 2016. "Graduates, Dropouts and Slow Finishers: The Effects of Credit Constraints on University Outcomes," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(3), pages 323-346, June.
    2. Hildete P. Pinheiro & Pranab K. Sen & Aluísio Pinheiro & Samara F. Kiihl, 2020. "A nonparametric approach to assess undergraduate performance," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(4), pages 538-558, November.
    3. Juliana Guimarães & Breno Sampaio, 2007. "The Influence Of Family Background And Individual Characteristics On Entrance Tests Scores Of Brazilian University Students," Anais do XXXV Encontro Nacional de Economia [Proceedings of the 35th Brazilian Economics Meeting] 092, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    4. Elisa Rose Birch & Paul W Miller, 2007. "The Characteristics of ‘Gap‐Year’ Students and Their Tertiary Academic Outcomes," The Economic Record, The Economic Society of Australia, vol. 83(262), pages 329-344, September.
    5. Grace Chia & Paul W Miller, 2007. "Tertiary Performance, Field of Study and Graduate Starting Salaries," Economics Discussion / Working Papers 07-12, The University of Western Australia, Department of Economics.
    6. Viviana Carcaiso & Leonardo Grilli, 2023. "Quantile regression for count data: jittering versus regression coefficients modelling in the analysis of credits earned by university students after remote teaching," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1061-1082, October.
    7. Mallik, Girijasankar & Shankar, Sriram, 2016. "Does prior knowledge of economics and higher level mathematics improve student learning in principles of economics?," Economic Analysis and Policy, Elsevier, vol. 49(C), pages 66-73.
    8. Bizon, Wojciech, 2018. "“Tell them it's easy”: Framing incentives in learning basic statistical problems," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 76(C), pages 76-81.
    9. Hildete P. Pinheiro & Rafael P. Maia & Eufrásio A. Lima Neto & Mariana Rodrigues-Motta, 2019. "Zero-one augmented beta and zero-inflated discrete models with heterogeneous dispersion for the analysis of student academic performance," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 749-767, December.
    10. Juliana Guimarães & Breno Sampaio, 2008. "Mind the Gap: Evidences from Gender Differences in Scores in Brazil," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807211527140, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    11. Girijasankar Mallik & John Lodewijks, 2010. "Student Performance in a Large First Year Economics Subject: Which Variables are Significant?," Economic Papers, The Economic Society of Australia, vol. 29(1), pages 80-86, March.

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