SciSpace 指南：如何设计研究问卷（步骤与示例）

A research questionnaire is more than a list of questions. It is a research instrument designed to collect information in a consistent way from a defined group of respondents.

A well-designed questionnaire can help you measure attitudes, behaviors, experiences, knowledge, or other variables relevant to your study. A poorly designed one can introduce ambiguity, bias, missing data, or questions that do not actually address your research objectives.

The good news is that questionnaire design becomes much more manageable when you treat it as a sequence of decisions - which is exactly what this guide tackles.

This guide explains how to design a research questionnaire, from defining your research objectives to pretesting the final instrument, with examples along the way.

What is a research questionnaire?

A research questionnaire is a structured set of questions or items used to collect data from participants. Depending on the research design, it can collect quantitative data, qualitative responses, or both.

Questionnaires are commonly used in fields such as psychology, education, healthcare, sociology, business, and market research. They may be administered online, on paper, by phone, or during an in-person or online interview.

A questionnaire is the instrument used to collect responses, while a survey is the broader research method or process that may involve sampling participants, administering the questionnaire, collecting data, and analyzing the results.

For example, a researcher studying student experiences with online learning might use a questionnaire containing:

• Multiple-choice questions about how often students attend online classes
• Rating-scale questions about satisfaction
• A question about technical difficulties
• An open-ended question asking students to describe their biggest challenge

The exact questions should depend on what the study is trying to measure.

7 steps for designing a research questionnaire

Step 1: Define your research objectives

Start with the research problem, not the questions.

Before writing anything, identify exactly what you need to learn from your respondents. Your research objectives should determine which concepts need to be measured and, ultimately, which questions belong in the questionnaire.

Ask:

• What is the main purpose of the study?
• What variables or concepts do I need to measure?
• What information is necessary to answer my research question?
• Which information can be obtained through a questionnaire?
• How will I use the responses during analysis?

For example, suppose your research question is:

How does the use of generative AI affect university students' academic writing practices?

You might decide that the questionnaire needs to measure:

1. How frequently students use generative AI for academic writing
2. What they use it for
3. Their perceived benefits
4. Their concerns about using it
5. Their self-reported changes in writing practices

This process is sometimes called operationalization: turning an abstract research concept into something that can be measured.

A useful rule is simple: every question should have a job. If you cannot explain why a question is needed, reconsider including it.

Step 2: Identify your target respondents

Your questionnaire should be designed for the people who will actually answer it.

Define your target population and consider characteristics that could affect how respondents understand or answer your questions. These might include age, education level, occupation, language proficiency, subject knowledge, or experience with the topic.

For example, a questionnaire about postgraduate research practices can use terminology that may be inappropriate for participants with no research experience.

Also consider how your participants will access the questionnaire. An online questionnaire may work well for a digitally connected population, while a paper or interviewer-administered format may be more appropriate in other settings.

Your sampling strategy is a separate methodological decision, but questionnaire design and sampling are closely connected. If the questionnaire is too long, difficult, or inaccessible for the people you are trying to study, your final sample may not represent your target population well.

Step 3: Decide what type of questions you need

Once you know what you want to measure, decide how respondents should provide their answers.

The two broad categories are open-ended and closed-ended questions.

Open-ended questions

Open-ended questions allow participants to respond in their own words.

Example:

What is the biggest challenge you face when conducting a literature review?

This format can reveal ideas that the researcher did not anticipate. It can be particularly useful when exploring a topic or collecting detailed experiences.

The trade-off is that open-ended responses take longer to answer and usually require more effort to analyze and code.

Closed-ended questions

Closed-ended questions provide predefined response options.

Example:

How often do you use reference management software?

• Never
• Rarely
• Sometimes
• Often
• Very often

These questions are easier to standardize and analyze, particularly when collecting quantitative data from many participants.

You can also combine the two formats. For example:

Which tool do you primarily use to manage references?

• Zotero
• Mendeley
• EndNote
• Another tool: ______
• I do not use reference management software

The final option gives respondents a way to provide an answer that the predefined categories do not capture. You can learn more about open and closed questions from the University of Iowa.

Step 4: Write clear and unbiased questions

Question wording can affect how participants interpret and answer an item. Keep questions concise, specific, and appropriate for your respondents.

Avoid leading questions, which encourage a particular response.

Leading:

How helpful was the university's excellent new research training program?

The word "excellent" already communicates an evaluation.

More neutral:

How helpful was the university's new research training program?

Also avoid double-barreled questions, which ask about two things at once.

Problematic:

How satisfied are you with the quality and accessibility of your university's research resources?

A respondent could be satisfied with the quality but dissatisfied with the accessibility. One answer cannot accurately represent both views.

Instead, separate the concepts:

How satisfied are you with the quality of your university's research resources?

and:

How satisfied are you with the accessibility of your university's research resources?

Watch out for jargon, unnecessarily complicated wording, double negatives, and vague terms such as "regularly" or "often" when a more precise timeframe would be useful.

For example, instead of:

Do you regularly read research papers?

you could ask:

Approximately how many research papers do you read each week?

The appropriate wording will depend on what you are trying to measure. Learn more about identifying bias from the New Jersey Institute of Technology.

Step 5: Choose appropriate response options and scales

The response format should match the variable you are measuring.

For attitudes or perceptions, rating scales such as Likert-type scales are commonly used.

Example:

I find it easy to identify relevant research papers.

• • Strongly disagree
  • Disagree
  • Neither agree nor disagree
  • Agree
  • Strongly agree

For frequency, you might use:

• Never
• Rarely
• Sometimes
• Often
• Very often

For categorical information, use clearly defined options:

What is your current level of study?

• • Undergraduate
  • Master's
  • Doctoral
  • Other

Response options should be clear, relevant, and non-overlapping where appropriate. If respondents could reasonably select more than one answer, say so explicitly.

For example:

Which methods do you use to find academic papers? Select all that apply.

This is different from:

Which method do you use most often?

The first allows multiple selections; the second asks for one primary choice.

You should also consider whether options such as "Other," "Not applicable," or "Prefer not to answer" are necessary for particular questions.

Step 6: Organize the questionnaire logically

A good questionnaire should feel like a coherent conversation rather than a random collection of questions.

Start with questions that are relatively easy to answer and help establish context. Then move toward more specific questions. Sensitive or particularly demanding questions can often be placed later, depending on the study.

For example, a questionnaire about postgraduate research experiences might follow this structure:

1. Participant eligibility
2. General research background
3. Research activities
4. Specific experiences or attitudes
5. Challenges and outcomes
6. Demographic questions, where relevant

Question order can matter because earlier questions may influence how respondents interpret later ones. This is sometimes referred to as a question-order or context effect. Learn more about this effect from this JSSM paper.

Use skip logic when certain questions only apply to particular respondents.

For example:

Have you used AI tools during your research?

• Yes → Continue to questions about AI use
• No → Skip to questions about general research practices

This keeps the questionnaire relevant and reduces unnecessary effort for participants.

Check out the survey methodology template to get an idea of questionnaire organisation.

Step 7: Pretest and revise the questionnaire

Do not assume a questionnaire works simply because it looks clear to you.

Pretesting gives you an opportunity to identify confusing wording, missing response options, technical problems, awkward transitions, and questions that respondents struggle to answer.

Ask people who resemble your target respondents to complete the questionnaire using the same format you plan to use in the actual study.

Afterward, ask questions such as:

• Was anything confusing?
• Were any questions difficult to answer?
• Did any response options seem missing?
• Were any questions repetitive?
• Did the questionnaire feel unnecessarily long?
• Did the instructions make sense?
• Did any question feel uncomfortable or leading?

You can also ask participants to explain how they interpreted particular questions. Sometimes a question that seems perfectly obvious to the researcher produces an interpretation they never anticipated.

For larger studies, a pilot study can go further by testing the planned procedures for sampling, administration, data collection, and analysis.

The purpose of pretesting is not to collect your final results early. It is to find problems while they are still inexpensive to fix.

Example of a research questionnaire

Imagine that your study examines students' use of AI tools for academic research. How will you design this study? A short questionnaire might include:

1. What is your current level of study?

• Bachelor's
• Master's
• Doctoral
• Other

2. How often do you use AI tools for academic research?

• Never
• Rarely
• Sometimes
• Often
• Very often

3. What do you primarily use AI tools for?

• Finding research papers
• Understanding papers
• Summarizing research
• Brainstorming ideas
• Writing or editing
• Other: ______

4. How confident are you in evaluating information produced by AI tools?

• Not at all confident
• Slightly confident
• Moderately confident
• Very confident
• Extremely confident

5. What is your biggest concern about using AI tools in academic research?

[Open response]

Notice that each question serves a different purpose. The questionnaire combines categorical questions, a rating scale, and an open-ended item rather than relying on one question format throughout.

Check out the statistics surveys template and the survey review template

Common mistakes to avoid in questionnaire design

Even a short questionnaire can run into methodological problems. Watch for these common issues:

• Asking unnecessary questions: More questions do not automatically produce better research. Remove items that do not contribute to your objectives.
• Using ambiguous wording: Words such as "frequently," "recently," or "a lot" can mean different things to different respondents.
• Writing leading questions: Avoid wording that signals which answer is expected or desirable.
• Combining multiple concepts: If a respondent needs to give separate opinions about two different things, use separate questions.
• Providing incomplete response options: Participants should have a reasonable way to report their actual answer.
• Making the questionnaire too long: Respondent fatigue can affect completion and data quality.
• Skipping the pretest: A few rounds of feedback can uncover problems that are difficult to spot when you are too close to the questionnaire.

Research questionnaire design checklist

Before distributing your questionnaire, check that:

If you're building a questionnaire as part of a larger research project, a research proposal template can also help you document the proposed instrument and explain how it fits into your methodology.

Final thoughts

Designing a research questionnaire is essentially an exercise in working backward from your research objectives. Decide what you need to measure, determine who can provide that information, choose suitable question formats, write each item carefully, organize the questionnaire, and test it before collecting your actual data.

There is no universal questionnaire that works for every study. A questionnaire for a clinical research project will have different requirements from one designed to study student experiences or consumer behavior. What matters is whether the instrument is appropriate for your research question, respondents, and planned analysis.

And if you're starting with a blank page, you don't necessarily have to stay there. A Survey Questionnaire Generator can help you generate an initial set of questionnaire questions that you can then refine against your research objectives, target population, and methodology. Treat the generated questions as a starting point—not a substitute for methodological judgment.

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