Why Use Ordinal Logistic Regression?

Ordinal logistic regression (often just called 'ordinal regression') is used to predict an ordinal dependent variable given one or more independent variables. ... As with other types of regression, ordinal regression can also use interactions between independent variables to predict the dependent variable.

What are the assumptions of ordinal logistic regression?

Assumptions. The dependent variable is measured on an ordinal level. One or more of the independent variables are either continious, categorical or ordinal. No Multi-collinearity - i.e. when two or more independent variables are highly correlated with each other.

What is the difference between logistic regression and ordinal regression?

Logistic regression is usually taken to mean binary logistic regression for a two-valued dependent variable Y. Ordinal regression is a general term for any model dedicated to ordinal Y whether Y is discrete or continuous.