IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/0003.html
   My bibliography  Save this article

Predicting success in college on the basis of the results of unified national exam

Author

Listed:
  • Poldin, Oleg

    (Higher School of Economics, Russia)

Abstract

This paper presents the results of econometric study on predicting first-year grade point average and dropout probability with national examination (EGE) results at the bachelor program in Economics in the Higher School of Economics. The use of a sum of four exams — math, social studies, Russian language and foreign language — shows worse fitting than a sum of three exams, excluding foreign language. In models with separate exams as regressors, the greatest effect on dependent variable provides math grades.

Suggested Citation

  • Poldin, Oleg, 2011. "Predicting success in college on the basis of the results of unified national exam," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 21(1), pages 56-69.
    • Handle: RePEc:ris:apltrx:0003
    as

    Download full text from publisher

    File URL: http://pe.cemi.rssi.ru/pe_2011_1_56-69.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rothstein, Jesse M, 2004. "College performance predictions and the SAT," Department of Economics, Working Paper Series qt59s4j4m4, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    2. Rothstein, J.M.Jesse M., 2004. "College performance predictions and the SAT," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 297-317.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Androushchak, Gregory & Poldin, Oleg & Yudkevich, Maria, 2012. "Peer effects in exogenously formed student groups," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 26(2), pages 3-16.
    2. Prakhov, Ilya, 2012. "The unified state examination and the determinants of academic achievement: Does investment in pre-entry coaching matter?," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 86-108.
    3. Ilya Prakhov, 2019. "The Determinants Of Expected Returns On Higher Education In Russia: A Human Capital Theory Perspective," HSE Working papers WP BRP 50/EDU/2019, National Research University Higher School of Economics.
    4. Peresetsky, Anatoly & Davtian, Misak, 2011. "Russian USE and olympiads as instruments for university admission selection," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 23(3), pages 41-56.
    5. Poldin, Oleg & Silaeva, Vera & Silaev, Andrey, 2014. "Comparing quality of admission to universities by the results of olympiads and unified state exams scores," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 36(4), pages 118-132.
    6. Evgeniya Popova & Marina Sheina, 2017. "Does Studying in a Strong School Guarantee Good College Performance?," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 1, pages 128-157.
    7. Zamkov, Oleg & Peresetsky, Anatoly, 2013. "Russian Unified National Exams (UNE) and academic performance of ICEF HSE students," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 30(2), pages 93-114.
    8. Ekaterina Kochergina & Ilya Prakhov, 2016. "Relationships between Risk Attitude, Academic Performance, and the Likelihood of Drop-outs," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 4, pages 206-228.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter Bergman, 2020. "Nudging Technology Use: Descriptive and Experimental Evidence from School Information Systems," Education Finance and Policy, MIT Press, vol. 15(4), pages 623-647, Fall.
    2. Jesse Rothstein & Albert Yoon, 2006. "Mismatch in Law School," Working Papers 29, Princeton University, School of Public and International Affairs, Education Research Section..
    3. Debopam Bhattacharya & Shin Kanaya & Margaret Stevens, 2017. "Are University Admissions Academically Fair?," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 449-464, July.
    4. Guyonne Kalb & Sholeh A. Maani, 2007. "The Importance of Observing Early School Leaving and Usually Unobserved Background and Peer Characteristics in Analysing Academic Performance," Melbourne Institute Working Paper Series wp2007n05, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    5. Beattie, Graham & Laliberté, Jean-William P. & Oreopoulos, Philip, 2018. "Thrivers and divers: Using non-academic measures to predict college success and failure," Economics of Education Review, Elsevier, vol. 62(C), pages 170-182.
    6. Michelle Rendall & Andrew Rendall, 2013. "Math Matters: Student Ability, College Majors, and Wage Inequality," 2013 Meeting Papers 1196, Society for Economic Dynamics.
    7. 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.
    8. Evgeniya Popova & Marina Sheina, 2017. "Does Studying in a Strong School Guarantee Good College Performance?," Voprosy obrazovaniya / Educational Studies Moscow, National Research University Higher School of Economics, issue 1, pages 128-157.
    9. Jesse Rothstein, 2019. "Inequality of Educational Opportunity? Schools as Mediators of the Intergenerational Transmission of Income," Journal of Labor Economics, University of Chicago Press, vol. 37(S1), pages 85-123.
    10. Mario I. Suárez & Alan R Dabney & Hersh C Waxman & Timothy P Scott & Adrienne O Bentz, 2021. "Exploring Factors that Predict STEM Persistence at a Large, Public Research University," International Journal of Higher Education, Sciedu Press, vol. 10(4), pages 161-161, August.
    11. Dur, Robert & Glazer, Amihai, 2008. "Subsidizing Enjoyable Education," Labour Economics, Elsevier, vol. 15(5), pages 1023-1039, October.
    12. Richard C. Atkinson and Saul Geiser, 2009. "Reflections on a Century of College Admissions Tests," University of California at Berkeley, Center for Studies in Higher Education qt49z7127p, Center for Studies in Higher Education, UC Berkeley.
    13. Gandil, Mikkel Høst & Leuven, Edwin, 2022. "College Admission as a Screening and Sorting Device," IZA Discussion Papers 15557, Institute of Labor Economics (IZA).
    14. Raj Chetty & John N. Friedman & Emmanuel Saez & Nicholas Turner & Danny Yagan, 2020. "The Determinants of Income Segregation and Intergenerational Mobility: Using Test Scores to Measure Undermatching," NBER Working Papers 26748, National Bureau of Economic Research, Inc.
    15. Conlin, Michael & Dickert-Conlin, Stacy & Chapman, Gabrielle, 2013. "Voluntary disclosure and the strategic behavior of colleges," Journal of Economic Behavior & Organization, Elsevier, vol. 96(C), pages 48-64.
    16. Yang, Guangliang, 2014. "Are all admission sub-tests created equal? — Evidence from a National Key University in China," China Economic Review, Elsevier, vol. 30(C), pages 600-617.
    17. Rothstein, Jesse, 2022. "Qualitative information in undergraduate admissions: A pilot study of letters of recommendation," Economics of Education Review, Elsevier, vol. 89(C).
    18. Geiser, Saul, 2015. "THE GROWING CORRELATION BETWEEN RACE AND SAT SCORES: New Findings from California," University of California at Berkeley, Center for Studies in Higher Education qt9gs5v3pv, Center for Studies in Higher Education, UC Berkeley.
    19. Milan Ranđelović & Aleksandar Aleksić & Radovan Radovanović & Vladica Stojanović & Milan Čabarkapa & Dragan Ranđelović, 2022. "One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education," Mathematics, MDPI, vol. 10(14), pages 1-23, July.
    20. Pedro Luis Silva, 2024. "Specialists or All-Rounders: How Best to Select University Students?," Journal of Human Capital, University of Chicago Press, vol. 18(2), pages 227-271.

    More about this item

    Keywords

    university admission; entrance examination; predicting GPA; Tobit model;
    All these keywords.

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:apltrx:0003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.