Patterns of Subject Mix in Higher Education Institutions: A First Empirical Analysis Using the AQUAMETH Database

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.

Defining typologies of universities through a DEA-MDS analysis: An institutional characterization for formative evaluation purposes

“…human resources, funding and facilities), as well as different potentialities for the interaction with their communities or third mission (e.g. Thursby and Kemp, 2002;Schoen et al, 2007;Lepori, Probst and Baschung, 2010;Berbegal-Mirabent, Lafuente and Solé, 2013). In other words, the characteristics and institutional configuration of universities lead to different strategic specialisation by mission (mission mix) and/or different mixes of mission activities (Schoen et al, 2007;Wright et al, 2008;Rossi, 2014;or Mora et al, 2015).…”

“…Bear in mind that subject mix is a strong source of horizontal diversity and that different fields of knowledge are related to different levels of input consumption (Daraio et al, 2011) and different output level potentials (Lepori, Probst, and Baschung, 2010).…”

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“…Integration between the two levels could be achieved in different ways. For studies focusing on the organizational level, disciplinary heterogeneity can be controlled through the repartition of students by fields within HEIs (Lepori, Baschung and Probst 2010) or by breaking down outputs by scientific domains (Waltman, Calero-Medina, Kosten, et al 2012). In turn, for studies at the disciplinary or individual level, if becomes now possible to control for organizational characteristics, as a possible explanation of observed differences, for example through the design of multi-level models (Lepori, Wise, Ingenhoff and Buhmann 2016).…”

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“…Therefore the study on which this article is built on was mainly carried out by means of case studies , chosen on the basis of four variables, namely the national variable, consisting in the opposition between federal and centralised political systems, the disciplinary profile of higher education institutions (HEIs) (specialist vs . generalist) (Lepori et al ., ), the size of the HEIs (large vs . small) and the type of scientific disciplines (soft vs .…”