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Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition

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  • Cristina Mollica
  • Lea Petrella

Abstract

The multi-cycle organization of modern university systems stimulates the interest in studying the progression to higher level degree courses during the academic career. In particular, after the achievement of the first level qualification (Bachelor degree), students have to decide whether to continue their university studies, by enrolling in a second level (Master) programme, or to conclude their training experience. In this work we propose a binary quantile regression (BQR) approach to analyse the Bachelor-to-Master transition phenomenon with the adoption of the Bayesian inferential perspective. In addition to the traditional predictors of academic outcomes, such as the personal characteristics and the field of study, different aspects of student's performance are considered. Moreover, the role of a new contextual variable, representing the type of university regulations experienced during the academic path, is investigated. The utility of the Bayesian BQR to characterize the non-continuation decision after the first cycle studies is illustrated with an application to administrative data of Bachelor graduates at the School of Economics of Sapienza University of Rome. The method favourably compares with more conventional model specifications concerning the conditional mean of the binary response.

Suggested Citation

  • Cristina Mollica & Lea Petrella, 2017. "Bayesian binary quantile regression for the analysis of Bachelor-to-Master transition," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(15), pages 2791-2812, November.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:15:p:2791-2812
    DOI: 10.1080/02664763.2016.1263835
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    Cited by:

    1. Chen, Cathy W.S. & Dong, Manh Cuong & Liu, Nathan & Sriboonchitta, Songsak, 2019. "Inferences of default risk and borrower characteristics on P2P lending," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    2. Sandra De Iaco & Sabrina Maggio & Donato Posa, 2019. "A Multilevel Multinomial Model for the Dynamics of Graduates Employment in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 149-168, November.
    3. Philipp Gareis & Tom Broekel, 2022. "The Spatial Patterns of Student Mobility Before, During and After the Bologna Process in Germany," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 113(3), pages 290-309, July.
    4. Bernardi, Mauro & Bottone, Marco & Petrella, Lea, 2018. "Bayesian quantile regression using the skew exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 92-111.

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