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Analysis of Irish third‐level college applications data

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  • Isobel Claire Gormley
  • Thomas Brendan Murphy

Abstract

Summary. The Irish college admissions system involves prospective students listing up to 10 courses in order of preference on their application. Places in third‐level educational institutions are subsequently offered to the applicants on the basis of both their preferences and their final second‐level examination results. The college applications system is a large area of public debate in Ireland. Detractors suggest that the process creates artificial demand for ‘high profile’ courses, causing applicants to ignore their vocational callings. Supporters argue that the system is impartial and transparent. The Irish college degree applications data from the year 2000 are analysed by using mixture models based on ranked data models to investigate the types of application behaviour that are exhibited by college applicants. The results of this analysis show that applicants form groups according to both the discipline and the geographical location of their course choices. In addition, there is evidence of the suggested ‘points race’ for high profile courses. Finally, gender emerges as an influential factor when studying course choice behaviour.

Suggested Citation

  • Isobel Claire Gormley & Thomas Brendan Murphy, 2006. "Analysis of Irish third‐level college applications data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 361-379, March.
    • Handle: RePEc:bla:jorssa:v:169:y:2006:i:2:p:361-379
    DOI: 10.1111/j.1467-985X.2006.00412.x
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    References listed on IDEAS

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    2. Murphy, Thomas Brendan & Martin, Donal, 2003. "Mixtures of distance-based models for ranking data," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 645-655, January.
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    Cited by:

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    2. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    3. Lee, Paul H. & Yu, Philip L.H., 2012. "Mixtures of weighted distance-based models for ranking data with applications in political studies," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2486-2500.
    4. Liam Delaney & Colm Harmon & Cathy Redmond, 2010. "Parental Education, Grade Attainment & Earnings Expectations among University Students," Working Papers 201035, Geary Institute, University College Dublin.
    5. Biernacki, Christophe & Jacques, Julien, 2013. "A generative model for rank data based on insertion sort algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 162-176.
    6. Cristina Mollica & Luca Tardella, 2021. "Bayesian analysis of ranking data with the Extended Plackett–Luce model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 175-194, March.
    7. Delaney, Liam & Harmon, Colm & Redmond, Cathy, 2011. "Parental education, grade attainment and earnings expectations among university students," Economics of Education Review, Elsevier, vol. 30(6), pages 1136-1152.
    8. Keane, Michael & Ketcham, Jonathan & Kuminoff, Nicolai & Neal, Timothy, 2021. "Evaluating consumers’ choices of Medicare Part D plans: A study in behavioral welfare economics," Journal of Econometrics, Elsevier, vol. 222(1), pages 107-140.
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    10. Cristina Mollica & Luca Tardella, 2017. "Bayesian Plackett–Luce Mixture Models for Partially Ranked Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 442-458, June.
    11. François Caron & Emily B. Fox, 2017. "Sparse graphs using exchangeable random measures," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1295-1366, November.

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