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

TL;DR

This paper introduces a Bayesian binary quantile regression model to analyze the decision of students to continue from Bachelor to Master studies, incorporating various predictors and contextual factors, and compares it with logistic regression.

Contribution

It develops a novel Bayesian binary quantile regression method for studying university progression decisions, including new contextual variables and performance aspects.

Findings

The Bayesian approach effectively characterizes non-continuation decisions.

The model outperforms traditional logistic regression in predictive accuracy.

Contextual university regulation variables influence student progression choices.

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 approach to analyze the Bachelor-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 the student's performance are considered. Moreover, a new contextual variable, indicating the type of university regulations, is taken into account in the model specification. The utility of the Bayesian binary quantile regression 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 and compared with a more conventional logistic regression approach.