Ordinal variables are a type of categorical variable where the categories have a meaningful order or ranking. This means that while the categories can be arranged from low to high or vice versa, the exact differences between the categories are not necessarily uniform or quantifiable. For example, a survey response scale such as “poor,” “fair,” “good,” and “excellent” is ordinal because the responses can be ranked, but the distance between these categories is not defined.

When analyzing ordinal variables, specific statistical methods are often employed, such as ordinal logistic regression. This method is suitable for predicting outcomes based on ordinal predictors and is particularly useful when the response variable is also ordinal. The proportional odds assumption is a key concept in ordinal logistic regression, which posits that the relationship between each pair of outcome groups is the same ²⁵.

In practice, ordinal variables can be encountered in various fields, including social sciences, health assessments, and market research, where subjective ratings or rankings are common.