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


  • Francesca DE BATTISTI


  • Giovanna NICOLINI


  • Silvia SALINI



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.

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  • Francesca DE BATTISTI & Giovanna NICOLINI & Silvia SALINI, 2008. "Methodological overview of Rasch model and application in customer satisfaction survey data," Departmental Working Papers 2008-04, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  • Handle: RePEc:mil:wpdepa:2008-04

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    References listed on IDEAS

    1. Giovanna Nicolini & Francesca De Battisti, 2006. "Methods for Summarizing the Rasch Model Coefficients," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1039, Universitá degli Studi di Milano.
    2. Karl Jöreskog, 1994. "On the estimation of polychoric correlations and their asymptotic covariance matrix," Psychometrika, Springer;The Psychometric Society, vol. 59(3), pages 381-389, September.
    3. David Andrich, 1978. "A rating formulation for ordered response categories," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 561-573, December.
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    More about this item


    Latent trait model; data reduction methods; ordinal variables;

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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