Nominal data is a type of categorical data that represents variables that have two or more categories without any natural ordering or hierarchy. It is named “nominal” because the values or categories are merely names or labels, with no quantitative meaning. In other words, nominal data cannot be ordered or ranked in a meaningful way.
Characteristics of nominal data include:
- The categories must be mutually exclusive, meaning each observation or individual can only belong to one category.
- The categories must be exhaustive, meaning every observation or individual must be able to be classified into one of the categories.
- The categories must be discrete and distinct, meaning there cannot be any overlap or ambiguity between them.
Examples of nominal data include:
- Gender: male or female
- Marital status: single, married, divorced, widowed
- Eye color: blue, green, brown, black, hazel
- Blood type: A, B, AB, O
- Race/ethnicity: Asian, Black or African American, Hispanic or Latino, White
- Religion: Christianity, Islam, Judaism, Buddhism, Hinduism, etc.
In summary, nominal data is a type of categorical data that represents variables without any natural order or hierarchy. It is used to classify observations or individuals into distinct, mutually exclusive categories that are exhaustive and discrete. Nominal data is often used in research studies and statistical analysis to categorize data and identify patterns or relationships between variables.