A nominal scale is a way of categorizing data without any inherent order or ranking. Think of it as simply labeling or naming different groups. A great example is eye color. You can categorize people as having blue eyes, brown eyes, green eyes, etc., but there's no inherent ranking or order to these categories; one isn't "better" than another.
Understanding Nominal Scales
Nominal scales are used to assign labels or names to different categories. They are the simplest type of measurement scale. The numbers used, if any, are merely labels and do not have any numerical meaning.
Here are some more examples of nominal scales:
- Gender: Male, Female, Other
- Marital Status: Single, Married, Divorced, Widowed
- Religious Affiliation: Christian, Muslim, Jewish, Buddhist, etc.
- Country of Origin: USA, Canada, Mexico, etc.
- Hair Color: Black, Brown, Blonde, Red, etc.
- Types of Fruit: Apple, Banana, Orange, Grape
- Yes/No Responses: These are binary variables, a simple type of nominal scale.
Importantly, as noted in the provided sources like Voxco, QuestionPro, and Statistics How To, nominal scales simply categorize data. There's no inherent order or numerical significance to the categories. You can't perform mathematical operations (like calculating an average) with nominal data.
As explained by GraphPad, nominal scales lack a natural order or ranking among categories. Scribbr further emphasizes that variables like gender and ethnicity are inherently nominal because they cannot be ranked.
