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Multidimensional item response theory models for dichotomous data in customer satisfaction evaluation

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  • Federico Andreis
  • Pier Alda Ferrari

Abstract

In this paper, multidimensional item response theory models for dichotomous data, developed in the fields of psychometrics and ability assessment, are discussed in connection with the problem of evaluating customer satisfaction. These models allow us to take into account latent constructs at various degrees of complexity and provide interesting new perspectives for services quality assessment. Markov chain Monte Carlo techniques are considered for estimation. An application to a real data set is also presented.

Suggested Citation

  • Federico Andreis & Pier Alda Ferrari, 2014. "Multidimensional item response theory models for dichotomous data in customer satisfaction evaluation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 2044-2055, September.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:2044-2055
    DOI: 10.1080/02664763.2014.907395
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    References listed on IDEAS

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    1. Pier Ferrari & Paola Annoni & Giancarlo Manzi, 2010. "Evaluation and comparison of European countries: public opinion on services," Quality & Quantity: International Journal of Methodology, Springer, vol. 44(6), pages 1191-1205, October.
    2. Mariagiulia Matteucci & Stefania Mignani & Bernard P. Veldkamp, 2012. "The use of predicted values for item parameters in item response theory models: an application in intelligence tests," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(12), pages 2665-2683, August.
    3. Richard J. Patz & Brian W. Junker, 1999. "Applications and Extensions of MCMC in IRT: Multiple Item Types, Missing Data, and Rated Responses," Journal of Educational and Behavioral Statistics, , vol. 24(4), pages 342-366, December.
    4. Jackman, Simon, 2001. "Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Checking," Political Analysis, Cambridge University Press, vol. 9(3), pages 227-241, January.
    5. Richard J. Patz & Brian W. Junker, 1999. "A Straightforward Approach to Markov Chain Monte Carlo Methods for Item Response Models," Journal of Educational and Behavioral Statistics, , vol. 24(2), pages 146-178, June.
    6. Pier Ferrari & Silvia Salini, 2011. "Complementary Use of Rasch Models and Nonlinear Principal Components Analysis in the Assessment of the Opinion of Europeans About Utilities," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 53-69, April.
    7. Sandip Sinharay, 2004. "Experiences With Markov Chain Monte Carlo Convergence Assessment in Two Psychometric Examples," Journal of Educational and Behavioral Statistics, , vol. 29(4), pages 461-488, December.
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