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High school grades and university performance: A case study

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  • Cyrenne, Philippe
  • Chan, Alan

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 is 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 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 officers at other universities as an input into estimating the subsequent performance of first year students.

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Bibliographic Info

Article provided by Elsevier in its journal Economics of Education Review.

Volume (Year): 31 (2012)
Issue (Month): 5 ()
Pages: 524-542

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Handle: RePEc:eee:ecoedu:v:31:y:2012:i:5:p:524-542

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Web page: http://www.elsevier.com/locate/econedurev

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Keywords: High school grades; University performance; HLM model;

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  1. Horowitz, John B. & Spector, Lee, 2005. "Is there a difference between private and public education on college performance?," Economics of Education Review, Elsevier, vol. 24(2), pages 189-195, April.
  2. Christopher F Baum, 2006. "An Introduction to Modern Econometrics using Stata," Stata Press books, StataCorp LP, number imeus, March.
  3. Smith, Jeremy & Naylor, Robin, 2005. "Schooling effects on subsequent university performance: evidence for the UK university population," Economics of Education Review, Elsevier, vol. 24(5), pages 549-562, October.
  4. Hakkinen, Iida & Kirjavainen, Tanja & Uusitalo, Roope, 2003. "School resources and student achievement revisited: new evidence from panel data," Economics of Education Review, Elsevier, vol. 22(3), pages 329-335, June.
  5. David J. Zimmerman, 2003. "Peer Effects in Academic Outcomes: Evidence from a Natural Experiment," The Review of Economics and Statistics, MIT Press, vol. 85(1), pages 9-23, February.
  6. Ermisch, John & Francesconi, Marco, 2001. "Family Matters: Impacts of Family Background on Educational Attainments," Economica, London School of Economics and Political Science, vol. 68(270), pages 137-56, May.
  7. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2000. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," NBER Working Papers 7831, National Bureau of Economic Research, Inc.
  8. Hanushek, Eric A, 1986. "The Economics of Schooling: Production and Efficiency in Public Schools," Journal of Economic Literature, American Economic Association, vol. 24(3), pages 1141-77, September.
  9. 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.
  10. 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.
  11. Wayne A. Grove & Tim Wasserman, 2004. "The Life-Cycle Pattern of Collegiate GPA: Longitudinal Cohort Analysis and Grade Inflation," The Journal of Economic Education, Taylor & Francis Journals, vol. 35(2), pages 162-174, April.
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Cited by:
  1. 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.
  2. Sandra E. Black & Jane Arnold Lincove & Jenna Cullinane & Rachel Veron, 2014. "Can You Leave High School Behind?," NBER Working Papers 19842, National Bureau of Economic Research, Inc.
  3. Trappey, Amy J.C. & Trappey, Charles V. & Liu, Penny H.Y. & Lin, Lee-Cheng & Ou, Jerry J.R., 2013. "A hierarchical cost learning model for developing wind energy infrastructures," International Journal of Production Economics, Elsevier, vol. 146(2), pages 386-391.

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