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How to test for goodness of fit in ordinal logistic regression models

Author

Listed:
  • Morten W. Fagerland

    (Oslo University Hospital)

  • David W. Hosmer

    (Stanford University)

  • Hajime Uno

    (University of Vermont)

Abstract

Ordinal regression models are used to describe the relationship between an ordered categorical response variable and one or more explanatory variables. Several ordinal logistic models are available in Stata, such as the proportional odds, adjacent-category, and constrained continuation-ratio models. In this article, we present a command (ologitgof) that calculates four goodness-of-fit tests for assessing the overall adequacy of these models. These tests include an ordinal version of the Hosmer–Lemeshow test, the Pulkstenis–Robinson chi-squared and deviance tests, and the Lipsitz likelihood-ratio test. Together, these tests can detect several different types of lack of fit, including wrongly specified continuous terms, omission of different types of interaction terms, and an unordered response variable.

Suggested Citation

  • Morten W. Fagerland & David W. Hosmer & Hajime Uno, 2017. "How to test for goodness of fit in ordinal logistic regression models," Stata Journal, StataCorp LP, vol. 17(3), pages 668-686, September.
    • Handle: RePEc:tsj:stataj:y:17:y:2017:i:3:p:668-686
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    Cited by:

    1. Dimitris Zavras, 2021. "Studying Satisfaction with the Restriction Measures Implemented in Greece during the First COVID-19 Pandemic Wave," World, MDPI, vol. 2(3), pages 1-12, July.
    2. Imbulana Arachchi, Janaki & Managi, Shunsuke, 2021. "Preferences for energy sustainability: Different effects of gender on knowledge and importance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    3. Adewale Henry Adenuga & Claire Jack & Ronan McCarry, 2023. "Investigating the Factors Influencing the Intention to Adopt Long-Term Land Leasing in Northern Ireland," Land, MDPI, vol. 12(3), pages 1-18, March.

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