Close-Ended Questions in Research: 40+ Examples Every Researcher Can Use

Close-Ended Questions in Research: When to Use Them (With 40+ Examples)

Close-ended questions are survey questions that give respondents a fixed set of answer options: yes/no, multiple choice, rating scales, rankings, so responses are structured, comparable, and quantifiable at scale. They are the backbone of quantitative research and the reason most survey questions can be turned into percentages, trends, and statistical comparisons at all.

Quick Answer

Close-ended questions in 20 seconds:

• Definition: Questions with predefined answer options: the respondent selects, never composes
• The 7 types: Yes/No, multiple choice, rating scale, Likert scale, ranking, matrix, dichotomous
• Why researchers use them: Quantifiable, comparable, fast to answer, cheap to analyse at scale
• The limitation: They can only measure what you thought to ask: discovery needs open-ends
• The design rule: Options must be exhaustive and mutually exclusive: every respondent has exactly one honest home
• In this guide: 42 copy-ready examples across all 7 types

Introduction

Every percentage you have ever seen in a research readout, "68% prefer", "42% would switch", "NPS of 31", exists because someone asked a close-ended question. Open-ends produce quotes; close-ends produce data. That is their superpower and, badly designed, their trap: a close-ended question with the wrong options does not fail loudly like a confusing open-end: it quietly manufactures clean-looking numbers about a choice nobody was actually making.

This guide covers the whole discipline: what close-ended questions are, the seven types and when each earns its place, 42 real examples you can lift straight into a questionnaire, the honest comparison with open-ends, the situations where close-ends are the wrong tool entirely, and the design mistakes that corrupt data while looking perfectly professional.

Why This Topic Matters for Brands

Question format is a data-quality decision wearing a UX costume:

• Close-ends decide what is measurable: Trends, segments, drivers, and statistical tests all require structured responses: the analysis you can run is set the moment the format is chosen
• Bad options manufacture false findings: A multiple-choice list missing the real answer forces respondents into the nearest wrong one: the data looks clean and describes nothing
• Respondent effort is a budget: Close-ends spend it efficiently: surveys built on them complete faster, drop less, and keep attention: which protects the respondent quality your sample paid for
• Comparability is compounding: The same close-ended question, asked identically across waves, builds trend lines: rewrite it once and the trend resets to zero
• AI analysis multiplied their value: Structured responses feed models natively: close-ended data is what makes real-time dashboards, driver analysis, and automated tracking possible without a coding step

What Are Close-Ended Questions?

Close-ended questions are survey questions that restrict responses to a predefined set of options: the respondent selects rather than composes. The format does three jobs simultaneously: it standardises responses (everyone answers in the same units), it quantifies them (selections become counts, percentages, and scores), and it scales them (a thousand or a million responses analyse identically).

The benefits, honestly stated:

• Fast to answer: seconds per question, which respondents repay with completion and attention
• Directly quantifiable: no coding, no interpretation layer between answer and analysis
• Comparable: across respondents, segments, waves, and markets
• Statistically usable: significance tests, driver models, and segmentation all run on structured data

The limitations, equally honestly:

• They measure the menu, not the mind: respondents can only tell you what your options allow
• No discovery: the surprising answer, the one you did not anticipate, has nowhere to live
• False precision risk: forced selections look like convictions even when they were coin flips
• Option-design sensitivity: the answer list IS the instrument: a flawed list is a flawed study

The 7 Types of Close-Ended Questions

• Yes/No: The binary workhorse: ownership, incidence, awareness, eligibility. Fastest to answer, bluntest in resolution
• Multiple choice: One selection (or several, if flagged) from a list: the format for behaviours, preferences, and categories: lives or dies on option quality
• Rating scale: Numeric evaluation on a defined range (1-5, 0-10): satisfaction, likelihood, importance: the trend-line format
• Likert scale: Agreement with a statement across 5 or 7 labelled points: the attitude and perception instrument
• Ranking: Ordering options by preference or priority: reveals relative importance that ratings flatten (everything can be "very important"; only one thing can be first)
• Matrix: A grid of items sharing one scale: efficient for batteries of related attributes: and the format most abused into straight-lining
• Dichotomous: Two mutually exclusive options beyond yes/no: true/false, agree/disagree, this/that: the forced-choice sharpener

42 Real Survey Examples, By Type

Yes/No (6 examples)

1. Have you purchased a mattress in the past 12 months? (Yes / No)
2. Do you currently subscribe to any OTT streaming service? (Yes / No)
3. Have you heard of [brand] before today? (Yes / No)
4. Did you compare prices online before your last purchase? (Yes / No)
5. Have you ever returned a product bought online? (Yes / No)
6. Do you take protein supplements at least once a week? (Yes / No)

Multiple Choice (6 examples)

1. Where did you buy your most recent mattress? (Offline store / Online marketplace / Brand website / Other)
2. Which of these snack brands have you purchased in the past month? Select all that apply. (Brand A / Brand B / Brand C / Brand D / None of these)
3. What triggered your last skincare purchase? (Ran out of current product / Saw an ad or post / Recommendation / Special offer / New product curiosity)
4. How do you usually pay for online purchases? (UPI / Credit card / Debit card / Cash on delivery / EMI)
5. Which factor mattered most in your last appliance purchase? (Price / Brand / Features / Reviews / Warranty)
6. What best describes your household? (Live alone / Couple / Family with children / Joint family / Shared accommodation)

Rating Scale (6 examples)

1. How satisfied are you with your current mattress? (1 = Very dissatisfied to 5 = Very satisfied)
2. How likely are you to recommend [brand] to a friend or colleague? (0-10)
3. How would you rate the delivery experience of your last online order? (1-5 stars)
4. How important is "natural ingredients" when choosing skincare? (1 = Not at all important to 5 = Extremely important)
5. How would you rate the value for money of your last purchase? (1-5)
6. How comfortable was the product on first use? (1 = Very uncomfortable to 5 = Very comfortable)

Likert Scale (6 examples)

1. "I trust online reviews when making purchase decisions." (Strongly disagree / Disagree / Neither / Agree / Strongly agree)
2. "I would pay more for a product with verified quality certifications." (5-point agreement)
3. "My current mattress gives me adequate back support." (5-point agreement)
4. "I prefer trying products before committing to a full purchase." (5-point agreement)
5. "Brands in this category all feel basically the same to me." (5-point agreement)
6. "I feel confident buying big-ticket items without seeing them physically." (5-point agreement)

Ranking (6 examples)

1. Rank these factors by importance when choosing a mattress: Comfort, Price, Durability, Brand reputation, Cooling features (1 = most important)
2. Rank these delivery options by preference: 10-minute delivery, Same-day, Next-day with free shipping, Scheduled slot
3. Rank these snack occasions by frequency in your household: Evening tea, Movie nights, Travel, Guests, Late-night
4. Rank these information sources by trust for product research: Friends and family, Online reviews, Influencers, Brand websites, In-store staff
5. Rank these mattress problems by how much they bother you: Sagging, Heat retention, Odour, Firmness mismatch
6. Rank these payment considerations: Lowest total price, No-cost EMI availability, Cashback, Easy returns

Matrix (6 grid items, one scale)

31-36. Rate each attribute of your current mattress (Very poor / Poor / Average / Good / Excellent):

• Comfort
• Firmness accuracy
• Temperature regulation
• Durability so far
• Value for money
• Hygiene and freshness

Dichotomous (6 examples)

1. For your next mattress, which matters more? (Comfort / Price)
2. Would you rather have a longer warranty or a lower price? (Longer warranty / Lower price)
3. When trying a new snack brand: (I usually plan it / It's usually an impulse)
4. Which describes you better? (I stick to brands I trust / I like trying new brands)
5. Given the choice: (Test in store, pay more / Buy online, pay less)
6. True or false: "I replaced my last mattress earlier than I expected to." (True / False)

Why this bank is structured the way it is: every example is decision-anchored (each maps to a real commercial question), category-grounded (drawn from live consumer research contexts), and design-clean (exhaustive, mutually exclusive, single-barrelled): lift them as written or use them as templates.

Open-Ended vs Close-Ended Questions

When NOT to Use Close-Ended Questions

• Discovery and exploration: New categories, unfamiliar audiences, early concept work: when you do not yet know the answer space, a fixed list is a blindfold
• The "why" behind a number: A 2-star rating's reasons deserve an open-end: pre-listed reasons recruit agreement, not truth
• Emotionally complex territory: Regret, trust, identity: forced options flatten exactly the texture the research needs
• When your option list is a guess: If the team debated the answer list for an hour, the honest move is an open-ended pilot first, close-ended measurement second
• Language you plan to reuse: Messaging and positioning research needs consumers' own words: options give them yours back

Common Mistakes (The Ones That Look Professional)

1. Non-exhaustive options: The real answer is missing, so respondents pick the nearest fiction: always test with an "Other" pilot, then close the list
2. Overlapping options: "1-5 years / 5-10 years": the 5-year respondent lives in both: mutually exclusive or broken
3. Double-barrelled items: "Was the product comfortable and durable?": two questions, one answer, zero meaning
4. Leading option order and framing: Positive options first, loaded wording, unbalanced scales: manufactured findings with clean formatting
5. Matrix abuse: Fifteen-row grids that train straight-lining: cap matrices at 6-8 rows and break batteries up
6. Scale inconsistency across waves: Switching 5-point to 10-point mid-tracker: the trend line dies quietly
7. No honest exits: Omitting "None of these" / "Don't know" forces fake answers: the data gains completeness and loses truth

PulseAI Research Insight: What Close-Ended Data Can and Cannot Catch

Close-ended questions are the measurement engine of modern research: and their structural limit is that they measure claims. A respondent selects "would pay ₹15,000+" in seconds: the format cannot tell you whether the selection is a conviction or a courtesy.

That gap is exactly where PulseAI Research's structure changes what close-ended data is worth. Fielding on Smytten's network of 30M+ active Indian consumers means structured questions run against people whose real behaviour is observable:

• Claims validated against behaviour: The Mattress? More Like "Mat-Stress" report asked close-ended spend-intent questions: and could check them: stated premium demand met the behavioural record of over one-third spending under ₹7,000, with only 8.3% showing real ₹25,000+ willingness: the same question, worth more, because the answer had an audit trail
• Cleaner inputs, same formats: Every example in the bank above performs better on verified category buyers: question 1's "Yes" means a real purchase, not a screener claim: the respondent quality layer beneath every format choice
• Structured data at AI speed: Close-ended responses feed automated analysis natively: which is how a fully structured study returns research-grade findings in 72 hours: the format's scalability, realised

The design lesson: close-ended questions are as good as two things: the option list you wrote, and the reality of the people answering: this guide covers the first: the sampling cluster covers the second.

How Brands Can Use This

1. Set the ratio deliberately: Mostly close-ended for measurement, one or two open-ends placed where the why lives: after key ratings and rejections: not scattered by habit
2. Run the two-property check on every option list: Exhaustive and mutually exclusive: every respondent must have exactly one honest home: the thirty-second audit that prevents most format failures
3. Pilot open, measure closed: When the answer space is uncertain, run a small open-ended pilot, build the option list from real language, then field the close-ended version at scale
4. Protect your trend instruments: Lock the wording and scale of tracked questions: version changes are data resets: document any change as a break in series
5. Steal from the bank: The 42 examples above are decision-anchored templates: adapt the category, keep the structure
6. Match format to the analysis you need: Rankings for priority trade-offs, Likert for attitudes, ratings for trends, dichotomous for forced clarity: choose the format from the decision backwards, the same discipline that governs the rest of your consumer behaviour research methods

Related Concepts

• Survey questions: The full instrument-design discipline this format family sits within
• Survey question types: The complete format landscape, open and closed
• Respondent quality: Why the people answering matter as much as the format asked
• Consumer insights: What well-structured data becomes once the climb to insight begins

FAQs

1.What are close-ended questions?

Close-ended questions are survey questions that restrict responses to a predefined set of options, such as yes/no, multiple choice, rating scales, or rankings. Respondents select rather than compose, which makes responses standardised, directly quantifiable, and comparable across people, segments, and time.

2.What are the types of close-ended questions?

Seven main types: yes/no questions, multiple choice, rating scales (numeric evaluation on a range), Likert scales (labelled agreement with statements), ranking questions (ordering by preference), matrix questions (grids of items sharing one scale), and dichotomous questions (forced choice between two options beyond yes/no).

3.What is an example of a close-ended question?

"How satisfied are you with your current mattress?" answered on a 1-5 scale is a rating-type close-ended question. "Have you purchased a mattress in the past 12 months? (Yes/No)" is the binary form. Both produce structured, countable responses rather than free text.

4.What is the difference between open-ended and close-ended questions?

Close-ended questions offer fixed options and produce quantifiable data: percentages, scores, and trends: analysed instantly at scale. Open-ended questions let respondents answer freely and produce qualitative texture: reasons, language, and surprises: at the cost of analysis effort and respondent fatigue. Strong surveys run mostly close-ended with deliberate open-ends where explanation matters.

5.When should you not use close-ended questions?

In discovery work where the answer space is unknown, when exploring the reasons behind a rating, in emotionally complex territory where fixed options flatten meaning, when the option list is a guess rather than evidence, and when the research needs consumers' own language for messaging. In those cases, open-ends or qualitative methods lead.

6.What are the advantages of close-ended questions?

They are fast for respondents (protecting completion and attention), instantly quantifiable without coding, comparable across waves and segments, statistically usable for significance testing and modelling, and natively compatible with AI-powered and real-time analysis: the properties that make them the backbone of quantitative research.

7.What are common mistakes with close-ended questions?

Seven recur: option lists that miss the real answer, overlapping ranges, double-barrelled items asking two things at once, leading wording or unbalanced scales, oversized matrix grids that train straight-lining, changing scales mid-tracker and breaking trends, and omitting honest exits like "None of these" or "Don't know".

8.How many options should a close-ended question have?

As many as the honest answer space requires and no more: typically 4-7 for multiple choice, 5 or 7 points for Likert scales, and 6-8 rows maximum for matrices. The governing test is not the count but the two properties: options must be exhaustive (every real answer has a home) and mutually exclusive (only one home each).