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Using data from universities with different structure of academic year to model student attrition

Author

Listed:
  • Gorbunova, Elena

    (National Research University Higher School of Economics, Moscow, Russian Federation;)

  • Ulyanov, Vladimir

    (National Research University Higher School of Economics, Moscow, Russian Federation;)

  • Furmanov, Kirill

    (National Research University Higher School of Economics, Moscow, Russian Federation;)

Abstract

Pooling the data from a number of universities into a single sample poses a problem for researchers who are performing regression analysis of student attrition. Academic year can be divided into different academic terms in different universities, and this discrepancy has to be taken into account. This paper considers a problem of using data with different periodicity in the framework of discrete-time event history analysis and gives an example of an estimated attrition model.

Suggested Citation

  • Gorbunova, Elena & Ulyanov, Vladimir & Furmanov, Kirill, 2017. "Using data from universities with different structure of academic year to model student attrition," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 45, pages 116-135.
    • Handle: RePEc:ris:apltrx:0313
    as

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    References listed on IDEAS

    as
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    4. Carl Lamote & Jan Van Damme & Wim Van Den Noortgate & Sara Speybroeck & Tinneke Boonen & Jerissa Bilde, 2013. "Dropout in secondary education: an application of a multilevel discrete-time hazard model accounting for school changes," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(5), pages 2425-2446, August.
    5. DesJardins, S. L. & Ahlburg, D. A. & McCall, B. P., 1999. "An event history model of student departure," Economics of Education Review, Elsevier, vol. 18(3), pages 375-390, June.
    6. Tatiana Melguizo & Gregory S. Kienzl & Mariana Alfonso, 2011. "Comparing the Educational Attainment of Community College Transfer Students and Four-Year College Rising Juniors Using Propensity Score Matching Methods," The Journal of Higher Education, Taylor & Francis Journals, vol. 82(3), pages 265-291, May.
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    More about this item

    Keywords

    event-history analysis; discrete hazard; student attrition;
    All these keywords.

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • I29 - Health, Education, and Welfare - - Education - - - Other

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