5 Types of "Invalid" Survey Questions 80% of Beginners Get Wrong

Last summer, Xiao Chen, a second-year master's student in my program, came to me with a thick stack of printed questionnaires, on the verge of tears. She had spent two full weeks stationed in the library, the cafeteria, and the classroom buildings, and had finally collected 327 responses. But when she eagerly entered the data into SPSS and began her analysis, she discovered that nearly half of the data was unusable.

"I asked 'What kinds of short videos do you usually watch?' and lots of people chose 'I like all of them' — I can't tell their priorities at all." "I asked 'Do you think this app is easy to use?' and 90% chose 'Yes,' but the product's retention rate is actually very low, so the data must be wrong." "I set up a multiple-choice question, but when the results came back I had no idea how to analyze them..."

Looking at her on the verge of tears, I sighed: the first lesson of questionnaire design isn't "what to ask" but "how to ask without getting it wrong." As a professional who has worked on national-level research projects for institutions such as the Chinese Academy of Sciences, the Chinese Academy of Social Sciences, and Peking University, I have seen too many studies "go off the rails" because of poorly designed questionnaires. Today, I've compiled these five most common yet most easily overlooked "invalid questions" — 80% of beginners have fallen into these traps, and I hope you won't be the next.

Pitfall 1: The Vague "Double-Barreled" Question

What's wrong here? One question asks two things at once — "price" and "appearance." If a respondent thinks the price is fair but dislikes the appearance, how should they answer? Choosing A would mislead you, and choosing C would blur their real attitude. The data collected from this kind of question reflects neither their assessment of price nor their assessment of appearance — it's a classic case of invalid data.

TLSUN tip: In the TLSUN system you can quickly duplicate a question, make small edits, and generate several similar single-choice questions.

Pitfall 2: The Clearly Leading Question

What's wrong here? "Smoking is harmful to health" is a widely accepted scientific fact. Stating this assertion directly in the question puts psychological pressure on respondents — disagreeing feels like denying science. A question with such an obvious value orientation induces respondents to choose the socially expected answer rather than their true opinion.

TLSUN tip: The TLSUN system supports randomized question ordering, so you can shuffle related questions to reduce the influence of contextual cues. It also supports trap questions (e.g., "For this question, please select B"), helping you identify invalid responses filled out carelessly.

Pitfall 3: The Question with Incomplete Options Made Up Off the Top of Your Head

What's wrong here? If you list only the four channels you happened to think of, but a respondent actually learned about you through "Zhihu Q&A" or a "Xiaohongshu post," what should they do? Either they pick an option at random (distorting the data) or they skip the question (losing a sample). Questions with incomplete options lead directly to completely wrong conclusions in channel analysis.

TLSUN tip: In the TLSUN system, multiple-choice questions support an "Other" option that expands automatically — when a respondent fills in "Other" content, the system records the text so it can be categorized later. It also offers randomized option ordering to avoid "primacy effect" bias caused by the order in which options are listed.

Pitfall 4: The Question Stuffed with Jargon

What's wrong here? "Perceived value," "revisit intention," "correlation" — these terms are common in academic papers, but ordinary respondents simply don't understand them. When filling out the questionnaire, they either pick an answer at random or give up altogether. Asking in language respondents don't understand is the same as not asking at all.

TLSUN tip: When designing your questionnaire, use the TLSUN system's pre-test feature to first distribute it to a small sample of your target audience and collect comprehension feedback. The system automatically records the average completion time per question — if a question takes unusually long, it means respondents are struggling to understand it, and you should refine the wording.

Pitfall 5: The Out-of-Context "Pure Imagination" Question

What's wrong here? Without seeing any membership benefits or knowing what the service actually includes, respondents have no way to give a reliable answer. This kind of abstract question, detached from any concrete scenario, tends to produce casually filled-in "made-up data" that is worthless for pricing decisions.

TLSUN tip: The TLSUN system supports randomized experimental question groups. You can design 2–3 versions with different benefit combinations, randomly assign respondents to different groups, and test price sensitivity under each combination. This A/B testing approach is far more precise than asking a single question.

Appendix: Questionnaire Design Self-Check List (Worth Bookmarking)

After designing each questionnaire, run through this checklist to help you avoid 90% of the pitfalls:

Question Level

• Does each question ask only one thing? (Avoid "double-barreled" questions)
• Is the wording neutral, with no leading bias?
• Do the options cover all possibilities? (Is there an "Other" option?)
• Are the options mutually exclusive? (No overlap)
• Will respondents understand all the terms used?

Questionnaire Level

• Does the introduction state the purpose of the survey and a confidentiality commitment?
• Are sensitive questions (income, age, etc.) placed at the end?
• Are there trap or lie-detector questions to identify invalid responses?
• Is the total completion time kept within 5–8 minutes?

Pre-Testing

• Did you have 3–5 people from your target audience do a trial run?
• Did you collect their feedback on how they understood the questions?
• Did you refine the questionnaire based on that feedback?

Why Choose the TLSUN Survey System?

As a professional platform that has supported national-level research projects for institutions such as the Chinese Academy of Sciences, the Chinese Academy of Social Sciences, and Peking University, the TLSUN Survey System doesn't just help you design your questionnaire — it accompanies you through the entire journey from data collection to a completed paper:

• Rich control options: questions and options can be randomized as needed; single-choice questions can include an "Other" option (with an input box); fill-in-the-blank questions can have numeric range controls, and more.
• Complex logic design: logic between questions can support "AND"/"OR" relationships across multiple conditions.
• Data quality control system: completion-time monitoring (down to each individual question), IP-address and device duplicate detection, designated respondents (people outside the allowed scope cannot fill it in), designated WeChat users, and designated regions.
• One-click analysis reports: automatically generates cross-tabulation and visual charts, with direct export to SPSS format.
• Free basic edition: completely free for student users, covering most thesis and social-practice needs.

Final Thoughts

Questionnaire design is both a science and an art. Avoid these five most common pitfalls and your questionnaire already outperforms 80% of beginners. But remember: a good questionnaire isn't designed — it's tested. No matter how careful you are, always run a small-scale trial first, refine it based on feedback, and only then distribute it at scale.

TLSUN has been deeply engaged in the research field for many years, and we have seen too many students forced to redo their theses or delay graduation because of questionnaire design mistakes. That is why we have distilled our experience from national-level research projects into this system — so that no one who takes their research seriously is let down by their tools.