How to Design a Research Questionnaire: Questions, Scales, Pilot Testing and Bias

A practical guide to questionnaire design for surveys, dissertations and research projects, from construct definition to pilot testing.

Begin with constructs, not questions

Before writing survey items, define what the study needs to measure. A construct such as job satisfaction, digital literacy or perceived stress is broader than a single question. Review theory and established instruments where appropriate. Every item should have a reason for being included and should connect to an objective, variable or descriptive characteristic needed for analysis.

Write one clear idea per item

Avoid double-barrelled questions such as “How satisfied are you with salary and management?” because respondents may have different views about each part. Use language appropriate to the target population, avoid unnecessary jargon and define time periods when they matter. Questions should not imply a preferred answer or assume facts that may not apply to all respondents.

Choose response scales deliberately

Likert-type response options can measure agreement, frequency, importance or satisfaction, but the labels should match the construct. Keep direction consistent where possible and decide whether a neutral midpoint is meaningful. Numerical codes are for analysis; respondents should see clear verbal options. For factual questions, categorical choices may be more appropriate than agreement scales.

Order questions to reduce burden and bias

Start with accessible questions that establish relevance and confidence. Group related items, place sensitive questions later where appropriate and use routing or “not applicable” options when needed. Long grids can increase straight-lining and fatigue. Mobile users should be considered because many online surveys are completed on small screens.

Pilot the questionnaire

Pilot testing checks whether questions are understood as intended, how long the survey takes and whether response options are complete. Cognitive interviewing can reveal why respondents interpret a question in an unexpected way. A pilot can also test data coding and the planned analysis workflow before full recruitment begins.

Plan reliability and validity before analysis

Internal consistency measures such as Cronbach’s alpha can be relevant for multi-item scales, but reliability does not prove validity. Use established measures where possible and explain any adaptations. Content validity, construct validity and measurement context should be considered. The final questionnaire should be included or documented sufficiently for the study to be reproducible.