What Is Nominal Data? Survey Questions, Examples, and Analysis

Nominal data is categorical data made up of names or labels that have no meaningful order. A survey respondent’s department, country, preferred product, or selected contact channel can all be nominal variables. You can count how many responses fall into each category, but you cannot place the categories on a meaningful scale or calculate a useful average from arbitrary codes assigned to their labels.

Nominal data is common in screening questions, demographics, market segmentation, and questions about behaviors or preferences. Correctly identifying it helps you choose suitable answer options, summaries, charts, and statistical tests. For the complete process around cleaning, segmenting, interpreting, and reporting responses, begin with our survey results analysis guide.

Characteristics of nominal data

The values are categories. They identify which group an observation belongs to, such as “email,” “phone,” or “live chat.”
The categories have no inherent order. Changing their display order does not change their meaning.
Numbers may be used as codes. If email is coded as 1 and phone as 2, phone is not twice email and the average of those codes has no interpretation.
A nominal variable can be binary or multicategory. Yes/no has two categories; preferred contact channel may have four or more.
Single-answer categories should be mutually exclusive and collectively exhaustive. Each respondent should be able to select one truthful option without two choices describing the same answer, and everyone should have an appropriate option.

Nominal and categorical are often used together, but categorical data also includes ordinal data, whose categories do have a meaningful order. The absence of order is what makes a categorical variable nominal.

Nominal data quick reference
What it answers	Which type, name, or category?
Category order	None
Useful summaries	Counts, percentages, and the modal category or categories
Useful chart	Bar chart
Comparing groups	Cross-tabulation with counts and row or column percentages
Statistical method	Depends on the research question, sampling, design, and assumptions

Nominal survey question examples

A nominal survey question asks respondents to select or provide a category rather than a rating or quantity. These examples illustrate several common uses.

Which contact channel do you prefer? Single selection

Which contact channel do you prefer?

1 of 8

Question format: Single selection
Example answer choices: Email, live chat, phone, self-service
Why the data is nominal: The channels are names without an inherent rank.

Which department do you work in? Dropdown list

Which department do you work in?

2 of 8

Question format: Dropdown list
Example answer choices: Customer success, finance, marketing, product, other
Why the data is nominal: Department identifies group membership, not position on a scale.

Did you use the help center before contacting support? Single selection

Did you use the help center before contacting support?

3 of 8

Question format: Single selection
Example answer choices: Yes, no, not sure
Why the data is nominal: The responses are unordered categories; yes/no alone is binary nominal data.

Which features does your team use? Multiple selection

Which features does your team use?

4 of 8

Question format: Multiple selection
Example answer choices: Logic, analytics, themes, integrations; select all that apply
Why the data is nominal: Each feature can be analyzed as its own selected/not-selected nominal variable.

Where did you first hear about us? Single selection

Where did you first hear about us?

5 of 8

Question format: Single selection
Example answer choices: Search engine, colleague, social media, event, other
Why the data is nominal: The source names different acquisition categories without ranking them.

Which country is your organization based in? Searchable dropdown

Which country is your organization based in?

6 of 8

Question format: Searchable dropdown
Example answer choices: Country names
Why the data is nominal: A country identifies group membership rather than a position on a scale.

Which type of team best describes your group? Single selection

Which type of team best describes your group?

7 of 8

Question format: Single selection
Example answer choices: Customer experience, human resources, product, research, other
Why the data is nominal: The choices classify teams into unordered categories.

Why did you choose this product? Multiple selection

Why did you choose this product?

8 of 8

Question format: Multiple selection
Example answer choices: Features, ease of use, support, price, recommendation; select all that apply
Why the data is nominal: Each reason is a category that can be recorded as selected or not selected.

Some values look numerical but remain nominal. A postal code, employee ID, telephone number, or jersey number identifies a category or entity; arithmetic with it would not describe a quantity. Conversely, age recorded as elapsed time and number of purchases are ratio data because their numerical differences and true zero have meaning. Age ranges such as 18–24 and 25–34 are ordinal categories because they have a meaningful order.

Marital or relationship status is also normally nominal because its categories have no inherent order. Ask only when it is relevant to the research objective, and offer options appropriate to the audience.

How to analyze nominal data

Start by confirming the valid base: the respondents included in that specific calculation after missing or inapplicable answers are handled consistently. Then produce a frequency table containing the count and percentage for every category, including zero-count categories when their absence is informative. The main descriptive measures are:

Frequency: the number of valid responses in each category.
Percentage: the frequency divided by a clearly stated denominator, usually the number of people who answered that question.
Modal category or categories: the category with the highest frequency, or all categories sharing that frequency when there is a tie.

For example, suppose 50 respondents name their preferred contact channel. If 22 select email, 15 select live chat, 8 select phone, and 5 select self-service, the results are 44%, 30%, 16%, and 10%, respectively. Email is the modal category. This simple frequency-and-percentage summary should come before any more advanced comparison.

Use a bar chart when readers need to compare category sizes. A pie or donut chart can show a simple part-to-whole relationship, but it becomes difficult to read when there are many categories or small differences. Always show the base size and make the denominator clear.

For multiple-response questions, distinguish percentage of respondents from percentage of selections. Percentages of respondents can add up to more than 100% because one person may select several options. Do not calculate the mean, median, standard deviation, or correlation from arbitrary numeric category codes.

SurveyLegend’s Live Analytics helps you review response distributions as answers arrive. For custom cross-tabs or statistical tests, export your survey data as CSV or Excel and continue in your preferred spreadsheet or statistics tool.

How to write a nominal survey question

Connect the question to the analysis. Decide why the category matters and how it will support a decision or segment before asking for it.
Ask one clear thing. “Which product did you buy most recently?” is easier to classify than a question that combines product, channel, and satisfaction.
Make single-answer choices mutually exclusive. A respondent should not fit two options. If overlap is intentional, clearly say that multiple answers may be selected.
Cover realistic answers. Add “Other” with a write-in field when the list cannot reasonably be complete, and use “Not applicable” or “I don’t know” only when each is a truthful response.
Handle sensitive questions carefully. Ask only for relevant information, use respectful categories, explain why it is needed when appropriate, and consider “Prefer not to answer.”
Do not imply a ranking. If the research question is about order or intensity, collect ordinal rather than nominal data.

In SurveyLegend, use a single-selection question when respondents should choose one category, a multiple-selection question when several answers can be true, or a dropdown list to present a longer set compactly. When choices have no logical sequence, randomizing their order can help reduce primacy or recency effects. Keep options such as “Other,” “None,” and “Prefer not to answer” in a stable, sensible position.

How to code nominal data

Coding turns category labels into consistent values for analysis. Preserve the original response data, create a codebook, and document every change. A basic codebook records the variable name, question wording, category label, stored code, missing-value rule, and any categories combined after collection.

Use one stable code for each category and retain the human-readable label.
Keep missing, not applicable, and prefer-not-to-answer values distinct.
For a select-all-that-apply question, create a separate selected/not-selected field for each option rather than treating every combination as a single category.
Review open-text “Other” responses before recoding them into an existing category; keep genuinely different answers separate.
Combine sparse categories only for a defensible analytical or privacy reason, and report what was combined.
Check the number of distinct categories to find unexpected or overly granular labels. For example, you might standardize “United States,” “USA,” and “U.S.” into one documented category while preserving the raw answers.

Nominal data analysis example using a cross-tab

Suppose 100 respondents answer the nominal question, “Which survey feature is most important to your team?” The table compares their answers by team type. The figures are illustrative, and the percentages use each row’s respondent count as the denominator.

Customer experience 8 (20%)

Customer experience

1 of 4

Design flexibility: 8 (20%)
Analytics and reporting: 16 (40%)
Integrations: 6 (15%)
Ease of use: 10 (25%)
Base: 40

Research 10 (28.6%)

Research

2 of 4

Design flexibility: 10 (28.6%)
Analytics and reporting: 14 (40%)
Integrations: 4 (11.4%)
Ease of use: 7 (20%)
Base: 35

Human resources 7 (28%)

Human resources

3 of 4

Design flexibility: 7 (28%)
Analytics and reporting: 5 (20%)
Integrations: 8 (32%)
Ease of use: 5 (20%)
Base: 25

All respondents 25 (25%)

All respondents

4 of 4

Design flexibility: 25 (25%)
Analytics and reporting: 35 (35%)
Integrations: 18 (18%)
Ease of use: 22 (22%)
Base: 100

Analytics and reporting is the overall mode at 35%. It is selected by 40% of customer experience and research respondents, while integrations is the most common answer among human resources respondents at 32%. Those differences describe this sample. Before generalizing them or treating them as evidence of an association, check the sampling method, cell sizes, and an appropriate statistical test.

Using the counts in this example, a chi-square test of independence gives χ²(6, N = 100) = 6.85, p = .335, with Cramér’s V = .19. One of the 12 expected cells is 4.5 and none is below 1, so the commonly used expected-count guideline for the chi-square approximation is met. The illustrative sample does not provide strong evidence of an association; this is not the same as proving that no association exists.

Statistical tests for nominal data

Choose a test from the research question and study design, not from the data type alone. Common starting points include:

Does one nominal variable match specified expected proportions? Chi-square goodness-of-fit test

Does one nominal variable match specified expected proportions?

1 of 6

Starting method: Chi-square goodness-of-fit test
Important check: Use counts and check how expected proportions and categories were specified.

Are two nominal variables associated? Chi-square test of independence

Are two nominal variables associated?

2 of 6

Starting method: Chi-square test of independence
Important check: Use counts, require independent observations, and inspect expected cell counts.

Do separately sampled groups have the same category distribution? Chi-square test of homogeneity

Do separately sampled groups have the same category distribution?

3 of 6

Starting method: Chi-square test of homogeneity
Important check: Confirm independent samples and inspect expected cell counts.

Is an independent 2 × 2 table too sparse for the chi-square approximation? Consider Fisher’s exact test

Is an independent 2 × 2 table too sparse for the chi-square approximation?

4 of 6

Starting method: Consider Fisher’s exact test
Important check: Check whether an exact method fits the sampling design and analysis plan.

Did the same people answer a binary question twice? McNemar’s test

Did the same people answer a binary question twice?

5 of 6

Starting method: McNemar’s test
Important check: Responses must be paired; this is not an independent-samples test.

Do predictors help explain a binary or multicategory nominal outcome? Binary or multinomial logistic regression

Do predictors help explain a binary or multicategory nominal outcome?

6 of 6

Starting method: Binary or multinomial logistic regression
Important check: This is a modeling method and usually requires additional statistical expertise.

A significant chi-square result says that the observed pattern is inconsistent with the null hypothesis under the test’s assumptions; it does not show which cells matter most, how strong the relationship is, or whether one variable caused the other. Review observed and expected counts, examine residuals when appropriate, and report an effect-size measure such as Cramér’s V for a contingency table.

Standard formulas may not be valid for weighted or complex survey samples with clustering or stratification. Very small expected cell counts, many sparse categories, multiple comparisons, and nonrepresentative sampling also require care. Consult a statistician when the inference will support a high-impact decision or formal research conclusion.

How to tell whether data is nominal

Ask whether the categories can be arranged meaningfully from lower to higher, or less to more, on the concept being measured. If not, the variable is nominal. If the categories have a meaningful order but the spacing between them is unknown, the variable is ordinal.

Nominal: preferred contact channel—email, phone, live chat, or self-service. Report counts, percentages, and the mode.
Ordinal: satisfaction—very dissatisfied, dissatisfied, neutral, satisfied, or very satisfied. Preserve the order and show the distribution; the median may also be meaningful.

Common nominal data mistakes

Averaging category codes or treating a higher code as a higher value.
Using overlapping answer options in a single-response question.
Omitting a realistic answer and forcing respondents into an inaccurate category.
Mixing missing, not applicable, and prefer-not-to-answer responses together.
Reporting percentages without the denominator or valid response count.
Forgetting that multiple-response percentages may exceed 100%.
Combining small categories after seeing the results without documenting the decision.
Using chi-square without checking expected counts, independence, and the survey design.
Describing an association between categories as proof of causation.

More about other data types

Nominal data is one of five survey data types covered in this guide collection. Continue with the guides below to choose methods that match the other variables in your survey.

What is nominal data?

Nominal data is categorical data made up of names or labels with no meaningful order. Examples include a respondent’s country, department, preferred contact channel, or a yes/no answer. The categories can be counted and compared, but arbitrary numeric codes assigned to them cannot be meaningfully ranked or averaged.

What is an example of a nominal survey question?

“Which contact channel do you prefer: email, live chat, phone, or self-service?” is a nominal survey question because the answer choices are different categories and none is inherently higher or lower than another.

Is gender nominal data?

Gender response categories are usually treated as nominal data because they have no inherent rank. Ask only when gender is relevant to the research objective, use inclusive options that fit the audience, allow an option to self-describe and multiple selections where relevant, and make sensitive questions optional when feasible.

How do you analyze nominal data?

Start with the valid count and percentage for each category, identify the modal category or categories, and use a bar chart to show the distribution. Use cross-tabulations to compare categories between meaningful groups. Do not calculate a mean or median from arbitrary numeric category codes because those codes are labels, not quantities.

Which statistical test is used for nominal data?

A chi-square goodness-of-fit test can compare one nominal variable with an expected distribution, while a chi-square test of independence can test the association between two categorical variables. Fisher’s exact test may be appropriate for a small 2 × 2 table, and paired binary responses may require McNemar’s test. The research design and test assumptions must be checked first.

What is the difference between a nominal scale and nominal data?

A nominal scale is the rule used to classify observations into distinct, unordered categories. Nominal data is the set of category labels or coded observations produced by applying that rule. For example, a preferred-contact-channel scale may define email, live chat, phone, and self-service as its categories; respondents’ selections are the nominal data.

Ready to turn feedback into your superpower?

Start creating better feedback surveys with SurveyLegend, start today!

Get started for free Book a demo