Qualitative Research Questionnaire: Examples & Design

What is a qualitative research questionnaire?

A qualitative research questionnaire is a structured or semi-structured instrument made up predominantly of open-ended questions that invite respondents to answer in their own words. Its purpose is interpretive: to understand experiences, perceptions, beliefs and the reasoning behind behaviour, rather than to quantify them. The output is textual data — sentences, anecdotes, explanations — which the researcher analyses for patterns of meaning rather than averages or percentages.

It sits within the broader family of methods of data collection and overlaps with the qualitative interview, but differs in one key respect: a questionnaire is delivered as a fixed set of written prompts (the wording is decided in advance), whereas an interview is a live, flexible conversation. A qualitative questionnaire is therefore a useful middle ground — it gives you depth and the respondent’s own voice, while remaining standardised, scalable and comparatively quick to administer across a larger group.

Crucially, “qualitative” describes the type of data and the analytic stance, not merely the absence of numbers. A well-designed qualitative questionnaire is built on an interpretivist or constructivist position: it assumes that meaning is socially constructed and that the researcher’s job is to understand participants’ subjective realities. Bryman and others stress that this epistemological grounding — not just question format — is what makes an instrument genuinely qualitative.

How a qualitative questionnaire differs from a quantitative one

The two instruments look superficially similar — both are lists of questions — but they answer different kinds of research question, sit on opposite sides of the quantitative vs qualitative research divide, and demand entirely different analysis. The table below summarises the core contrasts. If you also need to design the numeric counterpart, see our companion guide to the quantitative research questionnaire.

A practical consequence: a qualitative questionnaire of 8–12 well-crafted open questions answered by 15–30 purposively chosen participants can yield more than enough data for a strong dissertation chapter, whereas a quantitative survey typically needs hundreds of responses to support meaningful statistics.

When to use a qualitative questionnaire

Reach for a qualitative questionnaire when:

• Your topic is under-explored. There is little existing theory, so you need to discover what matters to people before you can measure anything — for example, how first-generation students experience belonging at university.
• You want the “why” behind a behaviour or attitude. A survey might tell you 40% of staff intend to leave; a qualitative questionnaire tells you why.
• Meaning and context are central. Topics involving identity, lived experience, emotion or sensitive subjects benefit from respondents’ own framing.
• You are generating hypotheses or theory. Open responses surface variables and relationships you can later test quantitatively in a mixed-methods design.
• You need depth but cannot interview everyone. A written qualitative questionnaire scales further than face-to-face interviews while preserving the respondent’s voice.

Avoid it when your question is genuinely about prevalence, magnitude or statistical relationships — there, a structured numeric instrument is the right tool.

Question types in a qualitative questionnaire

The art of a qualitative questionnaire lies in the kinds of questions you ask. Four types do most of the work.

1. Open-ended questions

The backbone of the instrument. They impose no fixed answers and invite the respondent to describe, explain or reflect. They typically begin with what, how, why, or with an invitation such as “Describe…” or “Tell me about…”. Example: “How would you describe your experience of working from home over the past year?”

2. Probing (follow-up) questions

Probes deepen an initial answer by asking for elaboration, an example, or clarification. In a written questionnaire you build probes in as planned follow-ups: “You mentioned feeling isolated — can you give a specific example of when that happened and what you did?” They convert a thin answer into rich data.

3. Scenario (vignette) questions

You present a short, realistic situation and ask how the respondent would think, feel or act. Vignettes are powerful for sensitive or abstract topics because they let people respond to a hypothetical “someone” rather than confessing directly. Example: “Imagine a colleague is being treated unfairly in a team meeting. How do you think you would respond, and why?”

4. Projective questions

Borrowed from psychology, projective techniques ask respondents to attribute thoughts or feelings to others, or to complete an open prompt, revealing attitudes they might not state directly. Examples include sentence completion (“The worst thing about online learning is…”) or third-person projection (“What do you think most people in your position really feel about…?”). They surface tacit or socially sensitive views.

Design principles for a qualitative questionnaire

Open questions are unforgiving: a poorly worded item produces unusable answers, and you cannot “re-run” the analysis on a different scale as you might with numbers. Apply these principles rigorously.

• Neutral wording. Phrase questions so they do not signal a “right” answer. “What are your views on the new policy?” is neutral; “Why do you think the unfair new policy is a problem?” is not.
• One idea per question. Avoid double-barrelled items that bundle two questions into one, e.g. “How satisfied are you with your supervisor and your workload?” — the respondent cannot answer both at once. Split them.
• Avoid leading and loaded questions. A leading question presupposes a conclusion (“How much did the excellent training help you?”); a loaded one embeds an assumption (“Where do you usually procrastinate?” assumes they do).
• Plain, accessible language. Strip jargon, acronyms and academic vocabulary. Write as you would speak to the participant.
• Logical sequencing. Order items so each flows naturally to the next, grouping related topics so the respondent is not jolted between unrelated themes.
• Funnelling. Open broad, then narrow. Begin with easy, general, non-threatening questions to build rapport, then move to specific and more sensitive ones once the respondent is engaged — the funnel technique.
• Keep it focused. Each open question demands effort; too many cause fatigue and shallow answers. Prioritise items that directly serve your research question.
• Allow space and silence. Provide generous open text boxes and explicit invitations to “add anything else you feel is important”, so respondents can raise what you did not anticipate.

“It is worth giving a lot of thought to the wording of individual questions… questions should not be too general, leading, contain double negatives, double-barrelled, ambiguous, or use jargon.” (Source: Saunders, Lewis & Thornhill, 2019)

A sample qualitative questionnaire you can adapt

The set below is for a business/education study on remote-working experiences, but the structure transfers to any topic. Notice the funnel: broad opener, specific middle, reflective close. Adapt the wording to your own discipline and research question.

Modes: self-completion vs interviewer-administered

A qualitative questionnaire can be delivered in two broad ways, and the choice shapes both data quality and practicality.

For most student dissertations, an online self-completion qualitative questionnaire offers the best balance of depth, reach and feasibility — provided the questions are exceptionally clear, because there is no interviewer to clarify them.

How to design one: a step-by-step process

1. Define your research question and objectives. Be explicit about what you need to understand. Every item must earn its place by serving this.
2. Specify the concepts to explore. Break the research question into 3–5 themes or sub-topics the questionnaire must cover.
3. Draft open questions for each theme. Write more than you need, choosing question types (open, probing, scenario, projective) to suit each theme.
4. Apply the design principles. Edit for neutrality, one idea per question, plain language; delete leading and double-barrelled items.
5. Sequence with funnelling. Order from broad and easy to specific and sensitive; group related items; end with a catch-all.
6. Add framing and instructions. Write a short introduction explaining purpose, confidentiality, consent and how to answer.
7. Pilot it. Test on 3–5 people similar to your sample. Check that questions are understood as intended and produce rich answers; revise anything ambiguous.
8. Finalise and administer. Choose your mode, recruit a purposive sample, and collect responses, monitoring for thin answers that may signal a weak question.

Sampling matters as much as wording: qualitative questionnaires use purposive, not random, selection — see sampling methods for choosing participants who can speak richly to your topic.

How to analyse the responses: thematic analysis

Because the data is textual, you analyse it for patterns of meaning rather than running statistics. The dominant approach is thematic analysis, most often using Braun and Clarke’s (2006) six-phase framework. In brief:

1. Familiarisation — read and re-read all responses, noting initial impressions.
2. Generating initial codes — label meaningful segments of text systematically across the whole dataset.
3. Searching for themes — cluster related codes into candidate themes.
4. Reviewing themes — check themes against the coded extracts and the full dataset; merge, split or discard.
5. Defining and naming themes — articulate the essence of each theme clearly.
6. Producing the report — write up with vivid, illustrative quotations tied back to your research question.

For a full walkthrough see our guide to thematic analysis. Where you want to count the frequency of specific words or categories within the text, content analysis is an alternative or complement. Whatever you choose, demonstrate rigour: keep an audit trail of coding decisions and consider inter-coder checks to support trustworthiness.

Strengths and limitations

Strengths:

• Captures depth, nuance and the respondent’s own language — the “why” behind attitudes.
• Flexible and exploratory; surfaces issues the researcher did not anticipate.
• Scales further and costs less than interviews while retaining qualitative richness.
• Ideal for new or sensitive topics and for generating hypotheses and theory.

Limitations:

• Time-consuming to analyse; coding text is labour-intensive and interpretive.
• Findings are not statistically generalisable — you aim for transferability, not representativeness.
• Answer quality varies; some respondents write little, especially in self-completion mode.
• Vulnerable to researcher subjectivity and to social-desirability bias in what people choose to disclose.
• Question wording must be near-perfect, because there is rarely a chance to clarify.

Common mistakes to avoid

• Writing closed questions by accident (“Do you like remote work?”) that invite one-word answers.
• Asking double-barrelled or leading questions that contaminate the data.
• Too many questions, causing fatigue and shallow responses.
• Skipping the pilot — the single most common reason for unusable data.
• Treating analysis as mere summarising rather than systematic coding for themes.

Related methodology guides

• Interviews in Research
• Qualitative Data Analysis

Frequently Asked Questions