Comprehensive analysis of the predictive validity of the university entrance score in Hungary

Abstract

An important problem in higher education is to find the most suitable admission procedure that can distinguish between students with high academic potential and future dropouts. Admissions usually rely on pre-enrolment achievement measures; therefore, it is crucial that these selection criteria have high predictive validity on academic achievement. In this study, we use sophisticated statistical learning methods, such as receiver operating characteristic curve analysis, logistic and Tobit regression to analyse the predictive validity of the Hungarian university entrance score on final university performance, in particular on degree completion and qualification. We place particular emphasis on drawing statistically grounded conclusions. The analysis is built on data of 21,547 undergraduate students from the Budapest University of Technology and Economics. We find that the current Hungarian centralised entrance score is a valid predictor, however, its predictive validity varies significantly across disciplines. We find that high school grades have strong predictive validity, and general knowledge is more important than program-specific knowledge. We also find that the academic performance of females is underpredicted and that of the males is overpredicted by the university entrance score.

Keywords:

• Predictive validity
• academic performance
• college admission
• range restriction

Acknowledgement

We are grateful for the kind support of Bálint Csabay, and Mihály Szabó.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research reported in this paper and carried out at BME has also been supported by the NRDI Fund (TKP2020 NC, Grant No. BME-NC) based on the charter of bolster issued by the NRDI Office under the auspices of the Ministry for Innovation and Technology. Marcell Nagy has been also supported by the ÚNKP-20-3 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.

Notes on contributors

Marcell Nagy

Marcell Nagy is a PhD student at the Department of Stochastics, Budapest University of Technology and Economics. His research focuses on data-driven network analysis and educational data science. He is also interested in applying statistical and machine learning methods on problems arising from industry, business, and health care. He is the deputy team leader of the Human and Social Data Science Lab.

Roland Molontay

Roland Molontay is a junior research fellow at the MTA-BME Stochastics Research Group operating at the Budapest University of Technology and Economics. His research focuses on network theory and data-driven educational research. He is the leader of the Human and Social Data Science Lab and the founder-coordinator of the Statistical and Mathematical Consulting Group of BME and he also leads a small research group conducting research on educational data science.