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Simple ways to interpret effects in modeling ordinal categorical data

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  • Alan Agresti
  • Claudia Tarantola

Abstract

We survey effect measures for models for ordinal categorical data that can be simpler to interpret than the model parameters. For describing the effect of an explanatory variable while adjusting for other explanatory variables, we present probability‐based measures, including a measure of relative size and partial effect measures based on instantaneous rates of change. We also discuss summary measures of predictive power that are analogs of R‐squared and multiple correlation for quantitative response variables. We illustrate the measures for an example and provide R code for implementing them.

Suggested Citation

  • Alan Agresti & Claudia Tarantola, 2018. "Simple ways to interpret effects in modeling ordinal categorical data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 210-223, August.
    • Handle: RePEc:bla:stanee:v:72:y:2018:i:3:p:210-223
    DOI: 10.1111/stan.12130
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    References listed on IDEAS

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    1. J. A. Anderson & P. R. Philips, 1981. "Regression, Discrimination and Measurement Models for Ordered Categorical Variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(1), pages 22-31, March.
    2. Alan Agresti & Maria Kateri, 2017. "Ordinal probability effect measures for group comparisons in multinomial cumulative link models," Biometrics, The International Biometric Society, vol. 73(1), pages 214-219, March.
    3. Olivier Thas & Jan De Neve & Lieven Clement & Jean-Pierre Ottoy, 2012. "Probabilistic index models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(4), pages 623-671, September.
    4. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
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