[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 only applies to data that meet the proportional odds assumption, the meaning of which can be exemplified as follows.
is an unobserved dependent variable (perhaps the exact level of agreement with the statement proposed by the pollster);
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 with most statistical models, maximum likelihood estimation or Bayesian inference are the most common ways of identifying the parameters.
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.
[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").