21 Dichotomous Questionnaire Examples (Updated 2026)

A dichotomous question offers exactly two answer options. Yes or no. True or false. Agree or disagree.

They are the simplest instrument in survey design, and the easiest to misuse. Used well, they screen respondents, route people through the right follow-up path, and produce clean data that needs no interpretation. Used badly, they force people into answers that misrepresent what they actually think, and you end up analysing noise with great precision.

This guide gives you 21 examples organised by use case, plus the single test that determines whether a yes/no question will produce reliable data.

What Makes a Question Dichotomous?

The term comes from the Greek for division into two parts. A dichotomous question belongs to the closed-ended family and permits exactly two mutually exclusive responses.

Common answer pairs:

• Yes / No
• True / False
• Agree / Disagree
• Eligible / Not eligible
• Fair / Unfair

The defining feature is not the wording but the structure: two options, no middle ground, no “it depends.”

The Binary Test

Before writing any yes/no question, ask one thing: is the underlying reality actually binary, or am I forcing it to be?

Compare these.

Genuinely binary: “Are you over 18 years of age?” Every respondent is or is not. There is no honest third answer.

Forced binary: “Do you like our product?” Some people like parts of it. Some are indifferent. Some like it but would not buy again. Forcing them to pick yes or no does not simplify the data — it fabricates it.

The distinction matters because forced-binary items produce answers that look clean and mean nothing. A 68% “yes” tells you nothing if a third of those respondents would have said “partly” given the option.

If a reasonable person might need “maybe,” “sometimes,” “not sure,” or more than one answer, use a scale or multiple choice instead.

21 Dichotomous Questionnaire Examples

Screening and Eligibility

Dichotomous questions are strongest here, because eligibility genuinely is binary.

1. Are you 18 years of age or older? Yes / No
2. Have you purchased from us in the past 12 months? Yes / No
3. Do you currently live in the region this survey covers? Yes / No

Consent and Compliance

Where a definitive record is required, ambiguity is a liability rather than a nuance.

4. Do you consent to take part in this study? Yes / No
5. Have you read and accepted the terms of service? Yes / No
6. Do you agree to be contacted about the results of this research? Yes / No

Behaviour and Factual Recall

Something either happened or it did not.

7. Did you use the mobile app in the last 30 days? Yes / No
8. Have you contacted our support team before today? Yes / No
9. Did you complete the onboarding checklist? Yes / No

Customer Experience

Note that these ask about outcomes rather than feelings — which is what keeps them genuinely binary.

10. Were you able to find what you were looking for today? Yes / No
11. Was your issue resolved during this conversation? Yes / No
12. Would you use this service again? Yes / No

Employee and HR

13. Do you know what is expected of you in your role? Yes / No
14. Have you had a performance conversation with your manager in the past six months? Yes / No
15. If we implemented the changes you suggested, would you consider working here again? Yes / No

That last one belongs in exit surveys, where it produces an unusually direct signal about whether a departure was preventable.

Events and Attendance

16. Is this your first time attending this event? Yes / No
17. Did you attend the opening keynote? Yes / No
18. Do you plan to attend next year? Yes / No

Question 16 is doing double duty: it is also a segmentation variable, letting you compare first-timers against returning attendees on every other answer.

Education and Assessment

19. True or false: the process described occurs inside the chloroplast. True / False
20. Did you complete the assigned reading before this session? Yes / No
21. Was the course material available to you on time? Yes / No

Their Real Superpower Is Routing

Most articles frame dichotomous questions as a data-collection tool. Their more valuable function is branching.

A single yes/no answer can send respondents down entirely different paths, which keeps surveys short and relevant. For example:

Have you contacted our support team before today? Yes → route to support experience questions No → skip that section entirely

Placed early, screening questions also prevent unqualified respondents from contaminating your dataset. Someone who has never used the product should not be answering questions about the product experience, and a dichotomous filter removes them in one step.

This is why dichotomous items cluster at the beginning of well-designed questionnaires. They are not there to produce insight. They are there to sort.

When Not to Use Them

Avoid a binary format when:

• You need intensity. “Are you satisfied?” loses the difference between mildly content and delighted. Use a scale.
• The honest answer is “sometimes.” Frequency belongs in a frequency scale.
• More than one option could be true. Use multiple selection.
• The topic is genuinely contested or complex. A binary framing on a nuanced question tends to produce whichever answer the wording nudges toward.
• You are measuring a demographic that is not actually binary. Older methodology texts list biological sex as a standard dichotomous example. Contemporary practice in most fields treats gender identity as non-binary and, where clinically relevant, asks about sex assigned at birth as a separate item. Check current guidance for your field rather than defaulting to a two-option demographic question.

Common Mistakes

Double-barrelled questions. “Was the product affordable and easy to use?” Someone who found it cheap but confusing cannot answer honestly. Split it into two questions.

Leading wording. “Do you agree that our new service is an improvement?” invites agreement. Neutral phrasing produces usable data.

Acquiescence bias. Respondents have a mild tendency to agree, particularly when tired or disengaged. Keep surveys short, and consider reversing the polarity of a few items to detect straight-lining.

No escape hatch where one is warranted. For factual questions about the past, a “don’t recall” option often produces better data than forcing a guess. This technically breaks the dichotomous format, which is the point — if people genuinely need a third option, the question was not binary.

Using them for everything. A questionnaire made entirely of yes/no items feels like an interrogation and produces shallow data. Pair them with open or scaled follow-ups.

Pairing for Depth

The most effective pattern is a dichotomous question followed by a conditional follow-up.

Was your issue resolved during this conversation? Yes → “What worked well?” (open text) No → “What is still outstanding?” (open text)

You get a clean quantitative measure you can trend over time, plus qualitative context on why the number moved. Neither question type delivers both alone.

Why Researchers Like Binary Data

There is a statistical reason beyond convenience.

Dichotomous responses produce a simple proportion — the percentage answering yes — which is easy to report, easy to compare across segments, and easy to trend across time periods. Binary outcome variables also feed directly into common analyses such as chi-square tests of independence and logistic regression.

Scale data is richer but requires more care about whether the intervals are genuinely equal. Binary data sidesteps that debate entirely, which is why it remains standard in educational assessment, clinical screening, and eligibility research.

Final Thoughts

Dichotomous questions are not a simplification tool. They are a precision tool for situations that are actually binary.

Before writing one, run the test: could a reasonable respondent need a third answer? If yes, use a different format. If no, a dichotomous question will give you cleaner, faster, more analysable data than anything else available.

Use them to screen, to route, and to record facts. Then pair them with scales and open questions to find out why the numbers look the way they do.

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