The Use of Large-Scale Administrative Data Sets to Monitor Progression from Vocational Education and Training into Higher Education in the UK: Possibilities and Methodological Challenges

Autor/inn/en	Hayward, Geoff; Hoelscher, Michael
Titel	The Use of Large-Scale Administrative Data Sets to Monitor Progression from Vocational Education and Training into Higher Education in the UK: Possibilities and Methodological Challenges
Quelle	In: Research in Comparative and International Education, 6 (2011) 3, S.316-329 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1745-4999
DOI	10.2304/rcie.2011.6.3.316
Schlagwörter	Higher Education; Statistical Analysis; Probability; Vocational Education; Foreign Countries; Comparative Analysis; Colleges; Risk; Dropouts; Dropout Rate; Labor Market; United Kingdom + Suchen Sie Ihr Suchwort? Hochschulbildung; Hochschulsystem; Hochschulwesen; Statistische Analyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Ausbildung; Berufsbildung; Ausland; College; Hochschule; Fachhochschule; Risiko; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Labour market; Arbeitsmarkt; Großbritannien
Abstract	This article describes two administrative data sources--UCAS applicant data and Higher Education Statistical Agency (HESA) data--and demonstrates how they can be utilised to monitor the progression of students from vocational and educational training (VET) programmes in to higher education (HE) in the UK. First the article provides a general discussion of the administrative data sets, their coverage and resulting limitations. Second, it illustrates how a combined HESA-UCAS data set can be utilised to provide a descriptive statistical analysis that demonstrates that including VET students within HE programmes widens participation for under-represented groups in the UK but shows that VET students are at a disadvantage compared with those progressing through academic pathways in terms of their probability of gaining access, which types of higher education institution (HEI) they gain access to, and an increased risk of dropping out during their first year of study. Third, multilevel modelling indicates that drop-out risks differ across HEIs and suggests that HEIs can take actions that will reduce the risk of VET students dropping out. Finally some conclusions about the utility of administrative data to answer substantive research questions are presented, with suggestions for improving the quality and access to such data for researchers. (Contains 6 tables and 16 notes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10