Analyzing and clustering students' application preferences in higher education

Zs. T. Kosztyán, É. Orbán-Mihálykó, Cs. Mihálykó, V. V. Csányi and Andras Telcs

Journal of Applied Statistics, 2020, vol. 47, issue 16, 2961-2983

Abstract: We present a framework based on a higher education application preference list that allows a different type of flexible aggregation and, hence, the analysis and clustering of application data. Preference lists are converted into scores. The proposed approach is demonstrated in the context of higher education applications in Hungary over the period of 2006–2015. Our method reveals that efforts to leverage center-periphery differences do not fulfill expectations. Furthermore, the student's top preference is very hard to influence, and recruiters may build their strategy on information about the first and second choices.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2019.1709052 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:47:y:2020:i:16:p:2961-2983

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2019.1709052

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().