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Customer Satisfaction Evaluation Using Multidimensional Item Response Theory Models

Author

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  • Federico ANDREIS
  • Pier Alda FERRARI

Abstract

In this paper Multidimensional Item Response Theory models, 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 and the problem of missing data is faced. An application to a real dataset is also presented.

Suggested Citation

  • Federico ANDREIS & Pier Alda FERRARI, 2015. "Customer Satisfaction Evaluation Using Multidimensional Item Response Theory Models," Departmental Working Papers 2015-25, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    • Handle: RePEc:mil:wpdepa:2015-25
    as

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    File URL: http://wp.demm.unimi.it/files/wp/2015/DEMM-2015_25wp.pdf
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    References listed on IDEAS

    as
    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. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. 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.
    4. Sturtz, Sibylle & Ligges, Uwe & Gelman, Andrew, 2005. "R2WinBUGS: A Package for Running WinBUGS from R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i03).
    5. 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.
    6. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
    7. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
    8. 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.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.

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    More about this item

    Keywords

    Binary Responses; Compensatory Models; Mirt; MCMC;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • D18 - Microeconomics - - Household Behavior - - - Consumer Protection
    • H40 - Public Economics - - Publicly Provided Goods - - - General
    • L88 - Industrial Organization - - Industry Studies: Services - - - Government Policy

    Statistics

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