Multinomial logistic regression is an important type of categorical data analysis. Specifically, it’s used when your response variable is nominal: more than two categories and not ordered.
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Multinomial Logistic Regression
Member Training: Multinomial Logistic Regression
December 30th, 2022 by TAF SupportHow to Decide Between Multinomial and Ordinal Logistic Regression Models
March 11th, 2019 by Karen Grace-MartinA 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 (more…)
Logistic Regression Models for Multinomial and Ordinal Variables
January 14th, 2009 by Karen Grace-MartinMultinomial 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 (more…)