Nominal and ordinal scales are two types of variable measurement scales. They can be useful when you’re conducting surveys and need to analyze the results. Nominal scales group by category, while ordinal surveys arrange data in a specific order. Depending on the type of data your analyzing, and your research questions, one scale may be more appropriate than another. Here’s an overview of the difference between nominal vs. ordinal scales, and examples of survey questions for both.
What is a nominal scale?
Nominal scales group data in categories, or names. The categories have corresponding numbers to help group the data together. Nominal data can include categories like age, gender, religion, ethnicity, yes/no, and more. There is no order or hierarchy associated with a nominal scale.
Nominal scales are good for overall categorization, like questions of brand preference, demographics, political preferences, home type, and more. For example, if you’re asking a yes/no question, the value of “no” might be one, and the value of “yes” might be two. However, those values have no relationship to each other. They’re just used to quickly categorize and sort data.
What is an ordinal scale?
Ordinal scales arrange data in specific orders—that is, in comparison to each other—and assign a rank for each variable. Likert scale questions, like “rate your satisfaction on a scale of 1 through 5,” are good examples of data that can be plotted on an ordinal scale. The five answers, starting with “bad” through “neutral” and ending with “excellent.” are all different categories. They’re also ranked.
However, unlike ratio scales, the difference between the categories isn’t quantitative. It’s not necessary for an “excellent” score to be five times better than a “bad” score. Instead, you use the categories and ranks to assign value to the answers. Unlike nominal scales, there is a hierarchy—but you don’t have to define the difference between the answers in order to analyze the data.
Ordinal scales have several benefits that may make you more inclined to use them in certain situations. They can help you understand sentiments, such as whether someone disagrees or “strongly disagrees” with a statement. Similarly, you can measure respondent perceptions (true, false, mostly true, mostly false), relative performance (efficient, more efficient, inefficient, very inefficient), and other opinions or experiences.
Nominal vs. ordinal scales for surveys
Depending on the type of data you’re collecting, you might use both nominal and ordinal survey questions in one survey. When you create your data analysis plan, you’ll know what kind of data you plan to collect and what kind of answers you hope to receive.
For example, you might use a nominal scale to categorize customer demographic data, from age and gender to education and income level. There’s no need to rank this data in terms of value—you simply want to understand who your customers are. Therefore, nominal scales are appropriate when you simply want to find out how many answers belong in specific categories.
Ordinal scales are great for value-based questions, like ranking the customer service experience or how likely someone would be to use your product and service again. Obviously, a positive experience has a higher rank than a negative experience.
Another example is drink size: you can ask your survey participants which size drink they’re most likely to order at a fast food restaurant: kid’s, small, medium, large, or extra large. These all have an order and a rank, but there’s no specific quantitative relationship between the sizes. You’re simply ranking the drink by size category.
Examples of nominal survey questions
These nominal survey question examples will help you categorize data:
- Age group (0-10, 20-30, 40-50, etc.)
- Location (country, state, city, neighborhood)
- Yes/no questions (e.g., would you use this service again?)
- Home type (single-family home, multigenerational home, apartment, mobile home, condo)
- Education level
- Political affiliation
- Smartphone brand
- Dietary preferences (omnivore, vegetarian, vegan, gluten-free, etc.)
- Hair and eye color
- Number of televisions in your home
Again, this data can also be used to create ordinal survey questions, depending on how you plan to use the data. It all depends on whether the answers are ranked or simply categorized. For example, if you’re measuring how long it takes to get through an educational program, you might rank answers depending on what’s most desirable, on an ordinal scale—but if you simply want to categorize respondents, you’d use a nominal scale.
Examples of ordinal survey questions
Try these ordinal survey questions next time you’re collecting nuanced data:
How was your experience with our product?
- Extremely satisfactory
- Extremely unsatisfactory
How do you feel about [public figure]?
- Very positive
- Neutral/no opinion
- Very negative
How likely are you to buy a product from [company] in the future?
- Very likely
- Somewhat likely
- Not sure/no opinion
- Very unlikely
How important is [feature] when buying a new [product]?
- Very important
- Neutral/nice to have
- Very unimportant
How fast were you able to resolve your customer service ticket?
- Less than one hour
- One to three hours
- Within 24 hours
- One to two days
- More than two days
In the question above, note that the time measurements are ranked (obviously, resolving an issue in less than an hour is desirable compared to it taking days to be completed), but the time measurements are not equal. They’re simply chosen categories that can be ranked according to your preferences.
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