Universities play a central role within society and should provide high quality services to students. Therefore, a careful evaluation of university services is necessary. This evaluation is complex because involves many partial aspects and can be assessed through a composite indicator. In the paper we propose a simple method for reducing the number of partial aspects underlying a composite indicator. A practical application to data from a sample survey conducted on last year students of the University of Padova is discussed. This survey considered the quality of many services, lecture rooms, library services, computer classrooms, reading rooms in libraries, study rooms, structure of exams, student socialization, reached skills and so forth. The method has been compared with principal component analysis. The results show that our method is worth of consideration since is markedly simpler to be applied than other dimension reduction methods and requires milder assumptions.

A composite indicator dimension reduction procedure with application to university student satisfaction

MAROZZI, Marco
2009

Abstract

Universities play a central role within society and should provide high quality services to students. Therefore, a careful evaluation of university services is necessary. This evaluation is complex because involves many partial aspects and can be assessed through a composite indicator. In the paper we propose a simple method for reducing the number of partial aspects underlying a composite indicator. A practical application to data from a sample survey conducted on last year students of the University of Padova is discussed. This survey considered the quality of many services, lecture rooms, library services, computer classrooms, reading rooms in libraries, study rooms, structure of exams, student socialization, reached skills and so forth. The method has been compared with principal component analysis. The results show that our method is worth of consideration since is markedly simpler to be applied than other dimension reduction methods and requires milder assumptions.
2009
Marozzi, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2521392
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