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High School Grades and University Performance: A Case Study

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  • Philippe Cyrenne

    (Department of Economics, The University of Winnipeg)

  • Alan Chan

Abstract

A critical issue facing a number of colleges and universities is how to allocate first year places to incoming students. The decision to admit students if often based on a number of factors, but a key statistic is a student's high school grades. This paper reports on a case study of the subsequent performance at the University of Winnipeg of high school students from 84 Manitoba High Schools. By tracking the University performance of a set of students admitted for the years 1997-2002, we are able to estimate the likelihood of success of subsequent students based on their characteristics as well as their high school grades. In doing so, we use a number of alternative estimators including a Least Squares Dummy Variable Model and a Hierarchical Linear Model. The methodology should be of interest to admissions o±cers at other universities as an input into estimating the subsequent performance of first year students.

Suggested Citation

  • Philippe Cyrenne & Alan Chan, 2010. "High School Grades and University Performance: A Case Study," Departmental Working Papers 2010-02, The University of Winnipeg, Department of Economics.
  • Handle: RePEc:win:winwop:2010-02
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    2. Zhaoyi Cao & Tim Maloney, 2017. "Decomposing Ethnic Differences in University Academic Achievement in New Zealand," Working Papers 2017-02, Auckland University of Technology, Department of Economics.
    3. Pengfei Jia & Tim Maloney, 2014. "Using Predictive Modelling to Identify Students at Risk of Poor University Outcomes," Working Papers 2014-03, Auckland University of Technology, Department of Economics.
    4. Philippe Cyrenne & Alan Chan, 2019. "The Determinants of Student Success in University: A Generalized Ordered Logit Approach," Departmental Working Papers 2019-03, The University of Winnipeg, Department of Economics.
    5. Cardak, Buly A. & Vecci, Joe, 2013. "Catholic school effectiveness in Australia: A reassessment using selection on observed and unobserved variables," Economics of Education Review, Elsevier, vol. 37(C), pages 34-45.
    6. Sezgin Polat & Jean-Jacques Paul, 2016. "How to predict university performance: a case study from a prestigious Turkish university?," Investigaciones de Economía de la Educación volume 11, in: José Manuel Cordero Ferrera & Rosa Simancas Rodríguez (ed.), Investigaciones de Economía de la Educación 11, edition 1, volume 11, chapter 22, pages 423-434, Asociación de Economía de la Educación.
    7. Hoffmann, Anna-Lena & Lerche, Katharina, 2016. "Class attendance and university performance," Center for European, Governance and Economic Development Research Discussion Papers 286, University of Goettingen, Department of Economics.
    8. Silva, Maria C.A. & Camanho, Ana S. & Barbosa, Flávia, 2020. "Benchmarking of secondary schools based on Students’ results in higher education," Omega, Elsevier, vol. 95(C).
    9. Danilowicz-Gösele, Kamila & Meya, Johannes & Schwager, Robert & Suntheim, Katharina, 2014. "Determinants of students' success at university," Center for European, Governance and Economic Development Research Discussion Papers 214, University of Goettingen, Department of Economics.
    10. Cyrenne, Philippe & Chan, Alan, 2012. "High school grades and university performance: A case study," Economics of Education Review, Elsevier, vol. 31(5), pages 524-542.
    11. Black, Sandra E. & Lincove, Jane & Cullinane, Jennifer & Veron, Rachel, 2015. "Can you leave high school behind?," Economics of Education Review, Elsevier, vol. 46(C), pages 52-63.
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    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior
    • L4 - Industrial Organization - - Antitrust Issues and Policies
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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