Skip to main content

Analyzing Evaluation Data: Modelling and Testing for Homogeneity

  • Conference paper
Data Analysis, Classification and the Forward Search

Abstract

In the evaluation process of a given service, different issues are worth of analysis. In first instance, it is interesting to assess how the evaluation responses changes over the time and whether there is an effect of the raters’ features. Secondly, when the service is made up by different items, it is important to verify if the satisfaction feelings of the users/consumers are the same with respect to all the dimensions. At this scope, the paper proposes a modelling approach for analyzing and testing ordinal/rating data. Some evidence from University services evaluation shows the usefulness of this procedure in a real case-study.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • CAGNONE, S., GARDINI, A. and MIGNANI, S. (2004): New developments of latent variable models with ordinal data. In: Atti delta XLII Riunione Scientifica SIS. CLEUP, Padova, 221–232.

    Google Scholar 

  • D’ELIA, A. (2003): A mixture model with covariates for ranks data: some inferential developments. Quaderni di Statistica, 5, 1–25.

    Google Scholar 

  • D’ELIA, A. (2004): New developments in ranks data modelling with covariates. In: Atti della XLII Riunione Scientifica SIS. CLEUP, Padova, 233–244.

    Google Scholar 

  • D’ELIA, A. and PICCOLO, D. (2005a): A mixture model for preferences data analysis. Computational Statistics & Data Analysis, 49, 917–934.

    Article  Google Scholar 

  • D’ELIA, A. and PICCOLO, D. (2005b): Un modello statistico per l’analisi dei giudizi di gradimento. In: Metodi. Modelli e Tecnologia delle Informazioni a Supporto delle Decisioni: Atti del Convegno MTISD2004. Franco Angeli, Milano, in press

    Google Scholar 

  • JÖRESKOG, K. and MOUSTAKI, I. (2001): Factor analysis of ordinal variables: a comparison of three approaches. Multivariate Behavioral Research, 36, 347–387.

    Article  Google Scholar 

  • McLACHLAN, G., and PEEL, G.J. (2000): Finite mixture models. Wiley, New York.

    MATH  Google Scholar 

  • MOUSTAKI, I. (2000): A latent variable model for ordinal data. Applied Psychological Measurement, 24, 211–223.

    Article  Google Scholar 

  • MOUSTAKI, I., JÖRESKOG, K. G. and Mavridis, D. (2004): Factor models for ordinal variables with covariate effects on the manifest and latent variables: a comparison of LISREL and IRT approaches. Structural Equation Modeling Journal, 11, 487–513.

    Article  Google Scholar 

  • PARASURMAN, A., ZEITHAML, V.A. and Berry, L.L. (1988): SERVQUAL: a multiple item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64, 12–40.

    Google Scholar 

  • PEÑA, D. (1997): Measuring service quality by linear indicators. In: P. Kunst, and J. Lemmink (Eds.): Managing service quality. Chapman Publishing Ltd., London, 35–51.

    Google Scholar 

  • PICCOLO, D. (2003): Computational issues in the E-M algorithm for ranks models estimation with covariates. Quaderni di Statistica, 5, 27–48.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Heidelberg

About this paper

Cite this paper

D’Elia, A., Piccolo, D. (2006). Analyzing Evaluation Data: Modelling and Testing for Homogeneity. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_34

Download citation

Publish with us

Policies and ethics