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Bayesian survival modelling of university outcomes

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  • Catalina A. Vallejos
  • Mark F. J. Steel

Abstract

The aim of this paper is to model the length of registration at university and its associated academic outcome for undergraduate students at the Pontificia Universidad Cat´olica de Chile. Survival time is defined as the time until the end of the enrollment period, which can relate to different reasons - graduation or two types of dropout - that are driven by different processes. Hence, a competing risks model is employed for the analysis. The issue of separation of the outcomes (which precludes maximum likelihood estimation) is handled through the use of Bayesian inference with an appropriately chosen prior. We are interested in identifying important determinants of university outcomes and the associated model uncertainty is formally addressed through Bayesian model averaging. The methodology introduced for modelling university outcomes is applied to three selected degree programmes, which are particularly affected by dropout and late graduation.
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Suggested Citation

  • Catalina A. Vallejos & Mark F. J. Steel, 2017. "Bayesian survival modelling of university outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 613-631, February.
    • Handle: RePEc:bla:jorssa:v:180:y:2017:i:2:p:613-631
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    File URL: http://hdl.handle.net/10.1111/rssa.12211
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    References listed on IDEAS

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    8. Elena Arias & Catherine Dehon, 2011. "The Roads to Success: Analyzing Dropout and Degree Completion at University," Working Papers ECARES ECARES 2011-025, ULB -- Universite Libre de Bruxelles.
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    Cited by:

    1. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    2. Diego Opazo & Sebastián Moreno & Eduardo Álvarez-Miranda & Jordi Pereira, 2021. "Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities," Mathematics, MDPI, vol. 9(20), pages 1-27, October.
    3. Samuel I. Watson & Richard J. Lilford & Jianxia Sun & Julian Bion, 2021. "Estimating the effect of health service delivery interventions on patient length of stay: A Bayesian survival analysis approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1164-1186, November.
    4. Moritz Berger & Thomas Welchowski & Steffen Schmitz-Valckenberg & Matthias Schmid, 2019. "A classification tree approach for the modeling of competing risks in discrete time," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 965-990, December.
    5. Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2018. "The economics of university dropouts and delayed graduation: a survey," GLO Discussion Paper Series 189, Global Labor Organization (GLO).
    6. Aina, Carmen & Baici, Eliana & Casalone, Giorgia & Pastore, Francesco, 2022. "The determinants of university dropout: A review of the socio-economic literature," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).

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    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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