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Separating location and dispersion in ordinal regression models

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  • Tutz, G.
  • Berger, M.

Abstract

In ordinal regression the focus is typically on location effects, potential variation in the distribution of the probability mass over response categories referring to stronger or weaker concentration in the middle is mostly ignored. If dispersion effects are present but ignored goodness-of-fit suffers and, more severely, biased estimates of location effects are to be expected since ordinal regression models are non-linear. A model is proposed that explicitly links varying dispersion to explanatory variables. It is able to explain why frequently some variables are found to have category-specific effects. The embedding into the framework of multivariate generalized linear models allows to use computational tools and asymptotic results that have been developed for this class of models. The model is compared to alternative approaches in applications and simulations. In addition, a visualization tool for the combination of location and dispersion effects is proposed and used in applications.

Suggested Citation

  • Tutz, G. & Berger, M., 2017. "Separating location and dispersion in ordinal regression models," Econometrics and Statistics, Elsevier, vol. 2(C), pages 131-148.
    • Handle: RePEc:eee:ecosta:v:2:y:2017:i:c:p:131-148
    DOI: 10.1016/j.ecosta.2016.10.002
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    References listed on IDEAS

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    Cited by:

    1. Gerhard Tutz & Moritz Berger, 2022. "Sparser Ordinal Regression Models Based on Parametric and Additive Location‐Shift Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 306-327, August.
    2. Roberto Colombi & Sabrina Giordano & Gerhard Tutz, 2021. "A Rating Scale Mixture Model to Account for the Tendency to Middle and Extreme Categories," Journal of Educational and Behavioral Statistics, , vol. 46(6), pages 682-716, December.
    3. Maria Iannario & Alfonso Iodice D’Enza & Rosaria Romano, 2024. "A hybrid approach for the analysis of complex categorical data structures: assessment of latent distance learning perception in higher education," Computational Statistics, Springer, vol. 39(1), pages 161-179, February.

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