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Multinomial Logistic Regression

How to Decide Between Multinomial and Ordinal Logistic Regression Models

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A great tool to have in your statistical tool belt is logistic regression.

It comes in many varieties and many of us are familiar with the variety for binary outcomes.

But multinomial and ordinal varieties of logistic regression are also incredibly useful and worth knowing.

They can be tricky to decide between in practice, however.  In some — but not all — situations you [Read more…] about How to Decide Between Multinomial and Ordinal Logistic Regression Models

Logistic Regression Models for Multinomial and Ordinal Variables

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Multinomial Logistic Regression

The multinomial (a.k.a. polytomous) logistic regression model is a simple extension of the binomial logistic regression model.  They are used when the dependent variable has more than two nominal (unordered) categories.

Dummy coding of independent variables is quite common.  In multinomial logistic regression the dependent variable is dummy coded into multiple 1/0 variables.  There is a variable for all categories but one, so if there are M categories, there will be M-1 dummy variables.  All but one category has its own dummy variable.  Each category’s dummy variable has a value of 1 for its category and a 0 for all others.  One category, the reference category, doesn’t need its own dummy variable as it is uniquely identified by all the other variables being 0.

The multinomial logistic regression then estimates a separate binary logistic regression model for each of those dummy variables.  The result is [Read more…] about Logistic Regression Models for Multinomial and Ordinal Variables