Using the Rasch model to assess a university service on the basis of student opinions

F Aiello, V Capursi - Applied Stochastic Models in Business and …, 2008 - Wiley Online Library
Applied Stochastic Models in Business and Industry, 2008Wiley Online Library
In this paper we use the Rasch model (RM) as a tool to measure the perceived quality of
student services at the Reception Office of an Italian university faculty. This paper has both a
substantive and a methodological aim. The former is concerned with measuring the service
quality of the Reception Office, while the latter concerns the definition and validation of an
instrument for measuring perceived quality. The sample comprised 273 students enrolled at
the Faculty of Economics at the University of Palermo (Aiello F. Il modello di Rasch per la …
Abstract
In this paper we use the Rasch model (RM) as a tool to measure the perceived quality of student services at the Reception Office of an Italian university faculty. This paper has both a substantive and a methodological aim. The former is concerned with measuring the service quality of the Reception Office, while the latter concerns the definition and validation of an instrument for measuring perceived quality. The sample comprised 273 students enrolled at the Faculty of Economics at the University of Palermo (Aiello F. Il modello di Rasch per la costruzione di uno strumento di misura della qualità di un Servizio. Ph.D. Thesis, University of Palermo, Palermo, 2005).
The RM is applied to produce specific measurements of the perceived quality for each service feature in order to conduct a critical analysis of the results. We sought to verify some important assumptions and desiderata of the RM: (i) Unidimensionality: in the user's opinion, are the single features of the service ‘good’ at defining the whole construct of global quality? (ii) Targeting: is the range of the measurements provided by the RM for each item able to cover all the possible satisfaction levels required by the students? (iii) Measurement of the perceived quality of each feature: is it possible to rank the items according to their level of perceived quality (which may be a good way of identifying the weaknesses of the service in order to allocate additional resources to make due adjustments)? (iv) Item separation: are the service features (items) redundant with respect to the satisfaction levels required by the students? (v) Differential item functioning: is the item‐difficulty hierarchy invariant across the student factors? In our opinion the last issue was of great interest as it can identify assessment differences. Indeed, there appeared to emerge a different severity of judgment towards the single features of the service from the students, according to their length of stay in the system. Copyright © 2008 John Wiley & Sons, Ltd.
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