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Residual diagnostics for interpreting CUB models

Author

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  • Francesca Di Iorio

    (Department of Theory and Methods of Human and Social Sciences, Statistical Sciences Section, University of Naples Federico II - Italy)

  • Maria Iannario

    (Department of Theory and Methods of Human and Social Sciences, Statistical Sciences Section, University of Naples Federico II - Italy)

Abstract

CUB models represent a new approach for the analysis of categorical ordinal data. The relevant domain of study is the specification and estimation of the behaviour of respondents when faced to ratings by analysing the relationship among ordinal scores and observed covariates. The increasing use of such models suggests to delve into the issue of appropriate residuals to be used for diagnostic purposes. In fact, the discreteness of the response variable discourages the use of standard regression paradigms. In this context, we propose the extension and implementation of a specific graphical methodology, known as binned residual plots, in order to check the adequacy of fitted CUB models and/or infer about improvements of the maintained model. Such proposals have been exemplified through the analysis of real data.

Suggested Citation

  • Francesca Di Iorio & Maria Iannario, 2012. "Residual diagnostics for interpreting CUB models," Statistica, Department of Statistics, University of Bologna, vol. 72(2), pages 163-172.
    • Handle: RePEc:bot:rivsta:v:72:y:2012:i:2:p:163-172
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    Cited by:

    1. Gennaro Punzo & Rosalia Castellano & Mirko Buonocore, 2018. "Job Satisfaction in the “Big Four” of Europe: Reasoning Between Feeling and Uncertainty Through CUB Models," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(1), pages 205-236, August.
    2. Romina Gambacorta & Maria Iannario, 2012. "Statistical models for measuring job satisfaction," Temi di discussione (Economic working papers) 852, Bank of Italy, Economic Research and International Relations Area.

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