Until now, what most researchers in HE have done is to choose between aggregate data at national system level provided by statistical offices, or detailed case-study data collected for single individual higher education institutions (HEIs). An important innovation of the Aquameth project has been the collection of meso-level data – that is, data at the level of whole HEIs – on a part of the European university system (six countries) in a systematic way, by applying broad common definitions of data categories across countries and collecting information already available at national level. Nevertheless, the Aquameth database which per se represents a very important result of the project has to be handled with care. It cannot be used in a ‘data mining’ way, but its exploitation needs a profound understanding of the meaning of the contained data and of their limitations, due both to conceptual problems and to the data collection procedures. This chapter deals with these kinds of issues with two major aims: to serve as a guide for those interested in further exploiting the database and to point out some major improvements in data which are urgently needed.
Bonaccorsi A., Daraio C., Lepori B. (2007). Indicators for the analysis of Higher Education Systems: some methodological reflections. CHELTENHAM : Edward Elgar Publisher.
Indicators for the analysis of Higher Education Systems: some methodological reflections
DARAIO, CINZIA;
2007
Abstract
Until now, what most researchers in HE have done is to choose between aggregate data at national system level provided by statistical offices, or detailed case-study data collected for single individual higher education institutions (HEIs). An important innovation of the Aquameth project has been the collection of meso-level data – that is, data at the level of whole HEIs – on a part of the European university system (six countries) in a systematic way, by applying broad common definitions of data categories across countries and collecting information already available at national level. Nevertheless, the Aquameth database which per se represents a very important result of the project has to be handled with care. It cannot be used in a ‘data mining’ way, but its exploitation needs a profound understanding of the meaning of the contained data and of their limitations, due both to conceptual problems and to the data collection procedures. This chapter deals with these kinds of issues with two major aims: to serve as a guide for those interested in further exploiting the database and to point out some major improvements in data which are urgently needed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.