In statistics, the ordered logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered by Peter McCullagh.[1] For example, if one question on a survey is to be answered by a choice among "poor", "fair", "good", "very good" and "excellent", and the purpose of the analysis is to see how well that response can be predicted by the responses to other questions, some of which may be quantitative, then ordered logistic regression may be used. It can be thought of as an extension of the logistic regression model that applies to dichotomous dependent variables, allowing for more than two (ordered) response categories.

The model and the proportional odds assumption

The model only applies to data that meet the proportional odds assumption, the meaning of which can be exemplified as follows. Suppose there are five outcomes: "poor", "fair", "good", "very good", and "excellent". We assume that the probabilities of these outcomes are given by p1(x), p2(x), p3(x), p4(x), p5(x), all of which are functions of some independent variable(s) x. Then, for a fixed value of x, the logarithms of the odds (not the logarithms of the probabilities) of answering in certain ways are:

The proportional odds assumption states that the numbers added to each of these logarithms to get the next are the same regardless of x. In other words, the difference between the logarithm of the odds of having poor or fair health minus the logarithm of odds of having poor health is the same regardless of x; similarly, the logarithm of the odds of having poor, fair, or good health minus the logarithm of odds of having poor or fair health is the same regardless of x; etc.[2]

Examples of multiple-ordered response categories include bond ratings, opinion surveys with responses ranging from "strongly agree" to "strongly disagree," levels of state spending on government programs (high, medium, or low), the level of insurance coverage chosen (none, partial, or full), and employment status (not employed, employed part-time, or fully employed).[3]

Ordered logit can be derived from a latent-variable model, similar to the one from which binary logistic regression can be derived. Suppose the underlying process to be characterized is

where is an unobserved dependent variable (perhaps the exact level of agreement with the statement proposed by the pollster); is the vector of independent variables; is the error term, assumed to follow a standard logistic distribution; and is the vector of regression coefficients which we wish to estimate. Further suppose that while we cannot observe , we instead can only observe the categories of response

where the parameters are the externally imposed endpoints of the observable categories. Then the ordered logit technique will use the observations on y, which are a form of censored data on y*, to fit the parameter vector .


As usual, maximum likelihood estimation or Bayesian inference are the most common ways of fitting parameters for such a model.[4] The estimated parameters indicate the direction and magnitude of the effect of each independent variable on the likelihood of the dependent variable falling into a higher or lower category.


Ordered logistic regressions have been used in multiple fields, such as transportation,[5] marketing[6] or disaster management.[7]

In clinical research, the effect a drug may have on a patient may be modeled with ordinal regression. Independent variables may include the use or non-use of the drug, as well as control variables such as demographics and details from medical history. The dependent variable could be ranked on the following list: complete cure, improved symptoms, no change, worsened symptoms, or death.[citation needed]

Another example application are Likert-type items commonly employed in survey research, where respondents rate their agreement on an ordered scale (e.g., "Strongly disagree" to "Strongly agree"). The ordered probit model provides an appropriate fit to these data, preserving the ordering of response options while making no assumptions of the interval distances between options.[8]

See also


  1. ^ McCullagh, Peter (1980). "Regression Models for Ordinal Data". Journal of the Royal Statistical Society. Series B (Methodological). 42 (2): 109–142. doi:10.1111/j.2517-6161.1980.tb01109.x. JSTOR 2984952.
  2. ^ Greene, William H. (2012). Econometric Analysis (Seventh ed.). Boston: Pearson Education. pp. 827–831. ISBN 978-0-273-75356-8.
  3. ^ Greene, William H. (2012). Econometric Analysis (Seventh ed.). Boston: Pearson Education. pp. 824–827. ISBN 978-0-273-75356-8.
  4. ^ Greene, William H.; Hensher, David A. (2010-04-08). Modeling Ordered Choices: A Primer. Cambridge University Press. ISBN 978-1-139-48595-1.
  5. ^ dell’Olio, Luigi; Ibeas, Angel; Cecín, Patricia (2010-11-01). "Modelling user perception of bus transit quality". Transport Policy. 17 (6): 388–397. doi:10.1016/j.tranpol.2010.04.006. ISSN 0967-070X.
  6. ^ Katahira, Hotaka (February 1990). "Perceptual Mapping Using Ordered Logit Analysis". Marketing Science. 9 (1): 1–17. doi:10.1287/mksc.9.1.1. ISSN 0732-2399.
  7. ^ Lovreglio, Ruggiero; Kuligowski, Erica; Walpole, Emily; Link, Eric; Gwynne, Steve (2020-11-01). "Calibrating the Wildfire Decision Model using hybrid choice modelling". International Journal of Disaster Risk Reduction. 50: 101770. doi:10.1016/j.ijdrr.2020.101770. ISSN 2212-4209.
  8. ^ Liddell, T; Kruschke, J (2018). "Analyzing ordinal data with metric models: What could possibly go wrong?" (PDF). Journal of Experimental Social Psychology. 79: 328–348. doi:10.1016/j.jesp.2018.08.009.

Further reading