Subject mix in higher education institutions: a first empirical analysis from the AQUAMETH database.

Details

Serval ID
serval:BIB_322DEF6672CD
Type
Article: article from journal or magazin.
Collection
Publications
Title
Subject mix in higher education institutions: a first empirical analysis from the AQUAMETH database.
Journal
Minerva
Author(s)
Lepori Benedetto  Baschung Lukas
ISSN
0026-4695
Publication state
Published
Issued date
2010
Volume
48
Number
1
Pages
73-99
Language
english
Notes
Paper presented at the ENID-PRIME Indicators conference, Oslo, 27-29 th May 2008.
Abstract
Teaching and research are organised differently between subject domains: attempts to construct typologies of higher education institutions, however, often do not include quantitative indicators concerning subject mix which would allow systematic comparisons of large numbers of higher education institutions among different countries, as the availability of data for such indicators is limited. In this paper, we present an exploratory approach for the construction of such indicators. The database constructed in the AQUAMETH project, which includes also data disaggregated at the disciplinary level, is explored with the aim of understanding patterns of subject mix. For six European countries, an exploratory and descriptive analysis of staff composition divided in four large domains (medical sciences, engineering and technology, natural sciences and social sciences and humanities) is performed, which leads to a classification distinguishing between specialist and generalist institutions. Among the latter, a further distinction is made based on the presence or absence of a medical department. Preliminary exploration of this classification and its comparison with other indicators show the influence of long term dynamics on the subject mix of individual higher education institutions, but also underline disciplinary differences, for example regarding student to staff ratios, as well as national patterns, for example regarding the number of PhD degrees per 100 undergraduate students. Despite its many limitations, this exploratory approach allows defining a classification of higher education institutions that accounts for a large share of differences between the analysed higher education institutions.
Keywords
Subject mix, Classification of higher education institutions, Generalist vs. specialist, Indicators, Staff composition
Create date
03/07/2008 10:35
Last modification date
20/08/2019 13:17
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