Ordered logit
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In statistics, the ordered logit model (also ordered logistic regression or proportional odds model), is a regression model for ordinal dependent variables. It can be thought of as an extension of the logistic regression model for dichotomous dependent variables, allowing for more than two (ordered) response categories.
The model makes the proportional odds assumption: the odds ratio for being in a chosen category or higher compared to being in a lower category is the same regardless of which category is chosen. This implies that if the ordinal variable were collapsed into two categories, the odds ratio would be the same regardless of the cut-off chosen for the collapsing.
[edit] See also
[edit] References
- Steve Simon (2004-09-22). "Sample size for an ordinal outcome". STATS - STeve's Attempt to Teach Statistics. http://www.childrens-mercy.org/stats/weblog2004/OrdinalLogistic.asp. Retrieved on 2008-03-04.
[edit] Further reading
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Please expand this article using the suggested source(s) below. More information might be found in a section of the talk page. |
- Woodward, Mark (2005). Epidemiology: Study Design and Data Analysis (2nd edition ed.). Chapman & Hall/CRC. ISBN 978-1584884156.
- Hardin, James; Hilbe, Joseph (2007). Generalized Linear Models and Extensions (2nd edition ed.). College Station: Stata Press. ISBN 978-1-59718-014-6.
