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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 . Multinomial Logit (Probit) Multinomial logit (probit) Models Nominal outcomes no intrinsic order (qualitative) Three or more Unordered categories Examples: Smoking status never, current, former smoker Marital status married, divorced, widowed, never married Multinomial Logit (Probit) Mo

Use ordered logistic regression because the practical implications of violating this assumption are minimal. Option 2: Use a multinomial logit model. 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

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