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Ordinal regression models: Problems, solutions, and problems with the solutions

Author

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  • Richard Williams

    (University of Notre Dame)

Abstract
Ordered logit/probit models are among the most popular ordinal regression techniques. However, these models often have serious problems. The proportional odds/parallel lines assumptions made by these methods are often violated. Further, because of the way these models are identified, they have many of the same limitations as are encountered when analyzing standardized coefficients in OLS regression, e.g., interaction terms and crosspopulation comparisons of effects can be highly misleading. This paper shows how generalized ordered logit/probit models (estimated via gologit2) and heterogeneous choice/location scale models (estimated via oglm) can often address these concerns in ways that are more parsimonious and easier to interpret than is the case with other suggested alternatives. At the same time, the paper cautions that these methods sometimes raise their own concerns that researchers need to be aware of and know how to deal with. First, misspecified models can create worse problems than the ones these methods were designed to solve. Second, estimates are sometimes implausible, suggesting that the data are being spread too thin and/or yet another method is needed. Third, multiple and very different interpretations of the same results are often possible and plausible. I will present guidelines for identifying and dealing with each of these problems.

Suggested Citation

  • Richard Williams, 2008. "Ordinal regression models: Problems, solutions, and problems with the solutions," German Stata Users' Group Meetings 2008 03, Stata Users Group.
  • Handle: RePEc:boc:dsug08:03
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    File URL: http://fmwww.bc.edu/repec/dsug2008/GSUG2008.pdf
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    File URL: http://fmwww.bc.edu/repec/dsug2008/GSUG2008-Handout.pdf
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    Citations

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    Cited by:

    1. Temitayo Adeyemo & Paul Amaza & Victor Okoruwa & Vincent Akinyosoye & Kabir Salman & Adebayo Abass, 2019. "Determinants of Intensity of Biomass Utilization: Evidence from Cassava Smallholders in Nigeria," Sustainability, MDPI, vol. 11(9), pages 1-16, April.
    2. Drakopoulos, Stavros A. & Grimani, Katerina, 2013. "Maslow’s Needs Hierarchy and the Effect of Income on Happiness Levels," MPRA Paper 50987, University Library of Munich, Germany.
    3. Drakopoulos, Stavros A. & Grimani, Katerina, 2015. "The Effect of Pay Cuts on Psychological Well-Being and Job Satisfaction," MPRA Paper 61195, University Library of Munich, Germany.
    4. Grimani, Katerina, 2014. "Labor earnings and Psychological well-being: An Empirical Analysis," MPRA Paper 57098, University Library of Munich, Germany.
    5. Chengxiang Zhuge & Chunfu Shao & Xia Li, 2019. "A Comparative Study of En Route Refuelling Behaviours of Conventional and Electric Vehicles in Beijing, China," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
    6. Md. Hashibul Hassan & Lubna Jebin, 2018. "Comparative Capability of Migrant and Non-Migrant Households: Evidence from Rural Bangladesh," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 8(5), pages 618-640, May.
    7. Fumarco, Luca & Baert, Stijn, 2018. "Younger and Dissatisfied? Relative Age and Life-satisfaction in Adolescence," MPRA Paper 89968, University Library of Munich, Germany.
    8. Adeyemo, T. & Okoruwa, V. & Akinyosoye, V., 2018. "Estimating causal effects of cassava based value-webs on smallholders welfare: a multivalued treatment approach," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277052, International Association of Agricultural Economists.
    9. Ross, Kara L. & Amanor-Boadu, Vincent, 2010. "They Say That They Are Healthy, But Are They? Health Perceptions In The U.S," 115th Joint EAAE/AAEA Seminar, September 15-17, 2010, Freising-Weihenstephan, Germany 116438, European Association of Agricultural Economists.

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