Ordinal Variable

Ordinal categories are categorical variables that have a meaningful order or ranking among them.

They are used when there is a need to represent data that can be ordered but where the intervals between the categories are not necessarily equal. Ordinal categories are commonly applied in scenarios such as grading systems, customer satisfaction surveys, and ranking systems. The technique works by assigning a rank or order to each category, allowing models to understand the relative position of each category.

For example, in a grading system, grades like A, B, C, D, and F can be represented as ordinal categories. In a customer satisfaction survey, responses like very satisfied, satisfied, neutral, dissatisfied, and very dissatisfied can be represented as ordinal categories.

Sometimes, ordinal categories are represented by numbers to facilitate arithmetic operations, but in many cases, arithmetic operations are not defined on these types of data. For instance, while it is possible to assign numbers to grades (e.g., 1 = very good, 2 = good, 3 = poor), it is not meaningful to perform arithmetic operations on these numbers.

The results will be unsatisfactory when a categorical variable is used to capture an unordered range of choices (e.g., 1 = Germany, 2 = France, 3 = UK), as the model will assume that the UK is larger than France and three times as large as Germany, while France would be treated as numerically between Germany and the UK. These hidden assumptions can lead to unexpected effects and should be avoided.

Ordinal categories are important because they provide a way to represent ordered data, enabling models to process and analyze data where the order of categories is significant.