Research Survey

Research survey template: design surveys that produce valid, publishable data

Research surveys have stricter requirements than feedback or satisfaction surveys. The data needs to be valid, the methodology needs to be defensible, and the questions need to avoid the biases that make results unreliable. Whether you are conducting academic research, UX studies, or market analysis, the design choices you make determine whether the data is useful.

This template covers question design for research contexts, common biases to avoid, and a framework for building surveys that hold up under scrutiny.

What makes research surveys different

Research surveys differ from operational surveys (customer feedback, employee satisfaction) in a few important ways:

• Validity matters more. A customer satisfaction survey with some bias is still useful. A research survey with bias produces misleading conclusions.
• Sample selection is critical. Who you survey determines whether results generalize to the broader population. Convenience samples (just surveying whoever is easy to reach) are often not sufficient.
• Question wording is rigorous. Research surveys avoid leading language, double-barreled questions, and ambiguous terms more strictly than operational surveys.
• Results need to be reproducible. Another researcher should be able to run the same survey and get comparable results.

Research survey template questions

This template is intentionally generic because research topics vary widely. Adapt the bracketed sections to your specific research area.

Screening and eligibility

1. What is your current experience level with [research topic]? | Multiple choice (Expert, Advanced, Intermediate, Beginner, No experience) | Required
2. How frequently do you [relevant behavior]? | Multiple choice (Daily, Weekly, Monthly, Rarely, Never) | Required

Use screening questions to filter respondents who meet your study criteria. If your research focuses on experienced practitioners, route beginners to a thank-you page.

Knowledge and awareness

3. How familiar are you with [concept/tool/method]? | Multiple choice (Very familiar, Somewhat familiar, Slightly familiar, Not at all familiar) | Required
4. How would you define [key term]? | Open text | Optional

Question 4 tests whether respondents understand the concepts you are asking about. If definitions vary widely, your results may reflect different interpretations of the questions rather than different experiences.

Attitudes and beliefs

5. To what extent do you agree with the following statement: "[research hypothesis statement]" | Likert (1-5, Strongly disagree to Strongly agree) | Required
6. How important is [factor] in your [decision/process]? | Likert (1-5) | Required
7. [Factor A] is more effective than [Factor B] for [outcome]. | Likert (1-5, Strongly disagree to Strongly agree) | Required

Behavior and practices

8. Which of the following [tools/methods/approaches] do you currently use? (Select all that apply) | Multi-select checkboxes | Required
9. How satisfied are you with your current approach to [activity]? | Rating scale (1-5) | Required
10. What challenges do you face when [doing the relevant activity]? | Open text | Required

Outcome measures

11. How would you rate the effectiveness of [intervention/tool/method] for [outcome]? | Rating scale (1-5) | Required
12. Has [intervention/change] improved your [outcome metric]? | Single choice (Significantly improved / Somewhat improved / No change / Somewhat worsened / Significantly worsened) | Required

Open-ended depth

13. What factors most influence your [decision/behavior]? | Open text | Required
14. Is there anything else you would like to share about your experience with [topic]? | Open text | Optional

Demographics (for research context)

15. What is your age range? | Multiple choice (standard ranges) | Optional
16. What is your professional background? | Multiple choice or open text | Optional
17. How many years of experience do you have in [field]? | Multiple choice (Less than 1, 1-3, 3-5, 5-10, 10+) | Optional

Avoiding common research survey biases

Leading questions

Leading questions suggest a "correct" answer. "Don't you agree that X is important?" pushes toward agreement. Rephrase as: "How important is X to you?"

Social desirability bias

Respondents answer in ways they think are socially acceptable rather than honestly. This is especially common with questions about behavior (exercise, spending, productivity). Frame questions to normalize all responses: "Some people prefer X, others prefer Y. Which is closer to your experience?"

Order effects

The order of questions and answer options influences responses. Earlier questions can frame how respondents interpret later ones. Answer options listed first get selected more often. Randomize answer option order when possible.

Double-barreled questions

"How satisfied are you with the speed and accuracy of [tool]?" is two questions. Speed and accuracy may have very different scores. Split them.

Acquiescence bias

Respondents tend to agree with statements rather than disagree (especially in Likert scales). Include reverse-coded items: if most statements are positive ("X is easy to use"), include some negative ones ("X requires too much effort"). This catches respondents who are just selecting "agree" down the line.

Sample size and distribution

Sample size depends on your analysis plan. For qualitative insights, 30-50 responses can be sufficient. For statistical tests (correlations, regressions, group comparisons), you typically need 100-300+, depending on the number of variables and expected effect size.

Probability sampling produces generalizable results. Random sampling from a defined population is the gold standard. When that is not feasible, acknowledge the limitations of your convenience sample.

Survey panels help with reach. Paid panels (Prolific, MTurk, UserTesting) let you target specific demographics and roles. They are widely accepted in published research, especially for exploratory studies.

For surveys aimed at your own user base, Formbricks supports targeted in-app surveys and website surveys that can reach specific user segments with configurable triggers.

Pilot testing your survey

Before launching to your full sample, run a pilot with five to 10 respondents:

• Measure completion time. If it exceeds 10 minutes, cut questions.
• Note where respondents hesitate or ask for clarification. Rephrase those questions.
• Check that response distributions are not clustered at one extreme (which may indicate a biased or confusing question).
• Verify that skip logic and routing work correctly.