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Models for Ordered and Unordered Categorical Variables

DUSTIN C. BROWN POPULATION RESEARCH CENTER Models for Ordered and Unordered Categorical Variables Objectives Introduce Models for multi-category outcomes Briefly discuss multinomial logit (probit) Models Briefly discuss ordinal logit (probit) Models Show examples in Stata Discuss practical issues, extensions, etc. Models for Multi-Category Outcomes These Models can be viewed as extensions of binary logit and binary probit regression. The dependent variable has three or more categories and is nominal or ordinal. Multinomial logit and Ordered logit Models are two of the most common Models .

This frees you of the proportionality assumption, but it is less parsimonious and often dubious on substantive grounds. Option 3: Dichotomize the outcome and use binary logistic regression. This is common, but you lose information and it could alter your substantive conclusions. Option 4: Use a model that does not assume proportionality.

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