Methodological overview of Rasch model and application in customer satisfaction survey data

This paper deals with the measurement of a service or product quality using Customer Satisfaction Survey results. Many different methods are used to analyse customer satisfaction data. Some use statistical models which estimate the relationship between the latent and manifest variables (LISREL, PLS, etc. ), whilst others use dimensionality reduction methods (FA, PCA, etc. ). All of these methods require a numerical quantification of the categories and consequently the distance between the numerical labels is fixed and the linear relationship between the variables implicitly assumed. Moreover these methods produce a customer satisfaction measure for each subject and an evaluation of its importance on the satisfaction level for each item. When analyzing quality and satisfaction levels together, the Rasch model (RM) appears to be particularly appropriate. A Likert scale is not required and non-linear relationships are involved. Moreover, a Rasch analysis can also act as a useful diagnostic tool for calibrating the questionnaire itself. In this paper we will present three different applications of the Rasch Model for the purposes of measuring quality and customer satisfaction levels. For each technique we will highlight its peculiarities, give an interpretation of the parameters used, analyse the model’s fit with the data and perform a critical analysis of the results.