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Using Validated Measures of High School Academic Achievement to Predict University Success

Author

Listed:
  • Tim Maloney

    (School of Economics, Auckland University of Technology)

  • Kamakshi Singh

Abstract

Administrative data from a New Zealand university are used to validate the National Certificate of Educational Achievement (NCEA) Rank Score used in university admissions and scholarship decisions. We find no statistical evidence to corroborate the specific weighting scheme used in this index. For example, our regression analysis suggests that too much weight is attached to the lowest category of credits in predicting both successful completion outcomes and letter grades. To show the potential importance of this validated measure of high school achievement, we run several simulations on these first-year student outcomes at this university. We show that the use of an alternative, empirically-validated measure of NCEA results to select students would lead to only slight improvements in course completion rates and letter grades. These higher entry standards would lead to declines in the proportions of Pacifica students, but minimal impacts on the proportion of Māori students enrolled at this university.

Suggested Citation

  • Tim Maloney & Kamakshi Singh, 2017. "Using Validated Measures of High School Academic Achievement to Predict University Success," Working Papers 2017-10, Auckland University of Technology, Department of Economics.
    • Handle: RePEc:aut:wpaper:201710
    as

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    File URL: https://www.aut.ac.nz/__data/assets/pdf_file/0004/136075/Economics-WP-2017-10.pdf
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    References listed on IDEAS

    as
    1. Joshua Angrist & Philip Oreopoulos & Tyler Williams, 2014. "When Opportunity Knocks, Who Answers?: New Evidence on College Achievement Awards," Journal of Human Resources, University of Wisconsin Press, vol. 49(3), pages 572-610.
    2. Julian R. Betts & Darlene Morell, 1999. "The Determinants of Undergraduate Grade Point Average: The Relative Importance of Family Background, High School Resources, and Peer Group Effects," Journal of Human Resources, University of Wisconsin Press, vol. 34(2), pages 268-293.
    3. Jill Johnes, 1997. "Inter-university variations in undergraduate non-completion rates: A statistical analysis by subject of study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(3), pages 343-362.
    4. Liane Moneta-Koehler & Abigail M Brown & Kimberly A Petrie & Brent J Evans & Roger Chalkley, 2017. "The Limitations of the GRE in Predicting Success in Biomedical Graduate School," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-17, January.
    5. Cohn, Elchanan & Cohn, Sharon & Balch, Donald C. & Bradley, James Jr., 2004. "Determinants of undergraduate GPAs: SAT scores, high-school GPA and high-school rank," Economics of Education Review, Elsevier, vol. 23(6), pages 577-586, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Academic at-risk students; Academic performance; Academic success; Economics of education;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

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