Question rewrites
12 leading question examples—and better survey rewrites
Spot leading survey questions with 12 before-and-after examples, a simple rewrite method, and a checklist for reviewing AI-generated drafts.
A leading question nudges someone toward a particular answer through its wording or assumptions. “How much do you love our easy checkout?” assumes both affection and ease. A more balanced question is “How easy or difficult was checkout?” The aim is to make unfavorable, uncertain and unexpected answers possible.
What makes a survey question leading?
A useful first check is to ask whether the wording contains the answer the team hopes to hear. Praise, social pressure and assumed benefits can all narrow the respondent’s task. The issue is the pressure built into the instrument, not whether the topic itself is positive or negative.
Pew Research Center’s methods guidance explains that wording and response options can affect answers. The rewrites below are MTB’s own teaching examples. They are suggestions to adapt and pretest, not evidence that a particular rewrite will eliminate every source of bias.
Sources: Pew guidance on question wording
12 before-and-after examples
These examples use fictional products and situations. Some questions have more than one flaw. Removing leading language may still leave an eligibility problem, an unclear time period or two judgments in one sentence.
| Leading or problematic draft | What to review | Suggested replacement |
|---|---|---|
| How much do you love our easy checkout? | Praise and assumed ease | How easy or difficult was checkout? |
| Don't you agree this offer is great value? | Pressure to agree | How would you rate the value of this offer? |
| Which benefit made you subscribe? | Assumes a benefit caused the choice | What most influenced your decision to subscribe? |
| How helpful was our support team? | Assumes helpfulness | How helpful or unhelpful was your last support interaction? |
| Wouldn't this feature save you time? | Assumes a time saving | How, if at all, would this feature change the time you spend on this task? |
| Why do you prefer our new headline? | Assumes a preference | Which headline, if either, do you prefer? |
| Most customers choose annual billing. Would you? | Social proof before the answer | Which billing option, if any, would you choose? |
| How much better is version B? | Assumes B is better | How does version B compare with version A for this task? |
| What do you like about the improved dashboard? | Assumes improvement and a positive view | What, if anything, stands out about this dashboard? |
| How satisfied are you with our fast and friendly service? | Praise plus separate attributes | Ask separately about satisfaction, speed and the interaction. |
| When will you start using the tool? | Assumes adoption | How likely or unlikely are you to try this tool in the next month? |
| Why did our pricing confuse you? | Assumes confusion | What, if anything, was unclear about the pricing? |
A three-pass rewrite method
Pass one: underline the assumptions. Look for adjectives such as excellent, effortless and improved. Look for a presumed action: why did you buy, when will you switch, or which benefit convinced you. Keep necessary factual context, but remove promotional framing from the measurement question.
Pass two: recover the intended measurement. Ask the researcher what decision the answer should inform. If the real concern is whether readers know the total price, a favorable-value rating will not resolve it. Ask for the price they understood, then compare that answer with the actual offer.
Pass three: test the exit routes. Can someone disagree, have no preference, lack experience, or say the information is insufficient? Provide those routes where appropriate. “None” and “cannot judge” should not be interchangeable.
Finally, read the question with its answer choices and the preceding screen. A neutral sentence can still follow a promotional paragraph that teaches the desired answer. Our question-order guide covers that separate problem.
Leading, loaded and double-barreled are different checks
A leading question steers toward an answer. A loaded question builds in a disputed or unverified assumption. A double-barreled question asks for a single response to separate judgments. These problems can occur together, but the repair depends on the actual issue.
Consider “How much do you love our cheap and reliable service?” Removing love leaves cheap and reliable, which can receive different answers. Asking two questions may be more useful than polishing the original sentence.
An occurrence of “and” is only a review signal. “Which research and development team do you work with?” can name one team. A mechanical checker cannot decide that every conjunction makes a question invalid.
How to use AI without automating the mistake
Give the assistant the purpose of the survey and the exact question. Ask it to identify the assumption before proposing a replacement. Request an explanation of how the rewrite changes the measure. A smoother sentence can quietly change the research question.
Try: “For each question, identify wording that suggests a preferred answer or assumes an experience. Preserve the intended construct. Suggest one neutral rewrite and one appropriate uncertainty option. If there are two constructs, split them. Do not invent product facts or call the result validated.”
Compare the rewrite with the original decision brief. In the example above, changing a comprehension check into an appeal question would be a failed rewrite even if the result reads beautifully. Use the assistant to expose choices for review; keep the final measurement decision explicit.
Before you send the survey
Have relevant people explain what they think each question is asking. Ask them how they chose their answer, particularly where wording has changed. Record revisions so the final questionnaire can be reproduced. The Census instrument-development standard treats pretesting as part of questionnaire development; this article’s checklist is a practical application, not an official instrument.
Sources: Census guidance on instrument development and pretesting
- Check one intended judgment per question.
- Read the complete answer list, not just the question.
- Keep criticism and uncertainty possible.
- Review context and question order.
- Retain the original and revised wording in the study record.
Common questions
Are all yes/no questions leading? No. “Did you use the search box?” can be a straightforward behavior question when the task and time period are clear. An open question such as “Why is our product better?” can be strongly leading.
Can software prove a question is unbiased? No. Automated flags can help reviewers find problems, but absence of a flag is not validation. Context, audience interpretation, sampling and administration still matter.
Should you remove every adjective? No. Some adjectives define the object being studied. The question is whether the wording provides necessary information or quietly tells the respondent how to judge it.
Sources and scope
- Writing Survey QuestionsPew Research Center
Methods guidance on wording, response options and order. The examples and review workflow here are original MTB applications.
- Statistical Quality Standard A2U.S. Census Bureau
Primary standards for developing and pretesting data-collection instruments; not certification of these templates.
Sources support the specific statements described above; they do not validate this publication’s rubric, guarantee a compliant execution, or replace context-specific professional advice.
What this page is: a research-methods guide, not a report of completed consumer fieldwork. Corrections and material revisions are recorded under the publication’s editorial standards.