Bayesian survival modelling of university outcomes

Catalina A. Vallejos, Mark F. J. Steel*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Dropouts and delayed graduations are critical issues in higher education systems world wide. A key task in this context is to identify risk factors associated with these events, providing potential targets for mitigating policies. For this, we employ a discrete time competing risks survival model, dealing simultaneously with university outcomes and its associated temporal component. We define survival times as the duration of the student's enrolment at university and possible outcomes as graduation or two types of dropout (voluntary and involuntary), exploring the information recorded at admission time (e.g. educational level of the parents) as potential predictors. Although similar strategies have been previously implemented, we extend the previous methods by handling covariate selection within a Bayesian variable selection framework, where model uncertainty is formally addressed through Bayesian model averaging. Our methodology is general; however, here we focus on undergraduate students enrolled in three selected degree programmes of the Pontificia Universidad Catolica de Chile during the period 2000-2011. Our analysis reveals interesting insights, highlighting the main covariates that influence students' risk of dropout and delayed graduation.

Original languageEnglish
Pages (from-to)613-631
Number of pages19
JournalJournal of the Royal Statistical Society: Series A
Volume180
Issue number2
Early online date14 Jul 2016
DOIs
Publication statusPublished - Feb 2017

Keywords

  • Bayesian model averaging
  • Competing risks
  • Delayed graduation
  • Proportional odds model
  • University dropout
  • GENERALIZED LINEAR-MODELS
  • REGRESSION-MODELS
  • G-PRIORS
  • COMPETING RISKS
  • LOGISTIC-REGRESSION
  • JEFFREYS PRIOR
  • EXPLORATION
  • ATTRITION
  • EDUCATION
  • MIXTURES

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