Sooner or later the methodology chapter of your dissertation forces the question: is your own data collection qualitative or quantitative? Plenty of students build an online questionnaire, add two free-text boxes and would happily write “a bit of both”. Supervisors rarely accept that.
Whether a survey is qualitative or quantitative is decided by the degree of standardisation, not by the number of open questions: if every respondent receives the same questions in the same order with the same answer options, the study is quantitative. It only becomes qualitative once wording, sequence and answers stay open and the individual case is interpreted. By the end of this article you can classify your own survey and defend that choice.
📌 Key takeaways
- Standardisation decides the label, not the open questions.
- A standardised online questionnaire stays quantitative, free-text boxes included.
- The ONS used qualitative and quantitative testing for Census 2021.
- Guest, Bunce and Johnson reached saturation within twelve interviews.
- An online panel is a self-selected group, not a random sample.
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Qualitative and quantitative surveys: the difference
A quantitative survey measures how often something occurs and tests expectations that were fixed before data collection began. A qualitative study reconstructs how individual people make sense of a situation, and works with few cases from which new expectations emerge.
A useful five-point contrast comes from the research methods guide of Teesside University, which draws on Denscombe's Good Research Guide. It states plainly that qualitative research “uses words or images as the data collected whereas quantitative research uses numbers”, and that qualitative work is “associated with small-scale studies” while quantitative work aims “to collect a large amount of data to be able to generalise”.
| Feature | Quantitative survey | Qualitative study |
|---|---|---|
| Data | numbers, or answers turned into numbers | words and images |
| Standardisation | questions and answer options fixed | open, develops in conversation |
| Scale | large-scale, built to generalise | small-scale, built for depth |
| Typical method | standardised questionnaire, online survey | in-depth interview, focus group |
| Researcher role | detached and objective | directly involved in the research |
| Typical result | how widespread something is | how people understand something |
Neither column is better research, they answer different questions. If you want to know how common an attitude is on your course, you need the quantitative route. If you want to understand where it comes from, you need the qualitative one. For the terms themselves, see our article on qualitative and quantitative research.
How to spot the degree of standardisation
You can read the degree of standardisation off three checks: are the questions fixed in advance, is their order fixed, and are the answer options fixed? Three yeses mean standardised and therefore quantitative. Three noes mean unstandardised and therefore qualitative.
Between those two poles sits a third level, and it is the one that appears most often in dissertations and gets described wrongly most often. All three in turn:
- Structured survey: everyone receives the same questionnaire with set answer options. Conditions stay constant, which is what makes statistical analysis possible in the first place.
- Semi-structured interview: a topic guide fixes the themes and their order while the answers stay open. The normal case for expert interviews.
- Unstructured interview: only the topic is fixed. The interviewer decides wording and sequence during the conversation. Typical of narrative interviews.
The word interview is the trap here, because it covers both ends of the scale. The Office for National Statistics defines in-depth interviews as “interviews with a small number of respondents, with explicit attention paid to the mental processes respondents use when answering survey questions”, yet the same organisation runs fully standardised interviews for its household surveys. Never let interview stand alone in your methodology chapter, always add structured, semi-structured or in-depth.
💡 Tip
Write the degree of standardisation into your methodology chapter instead of the label. “Standardised online survey with closed questions and two open follow-ups” survives any viva question. “Qualitative-quantitative hybrid” invites one.
Does a questionnaire with open questions count as qualitative?
A questionnaire with open questions is not a qualitative study. What matters is not the answer format but whether every respondent gets the same questions in the same order. If the questionnaire stays standardised, the free-text answers belong to a quantitative dataset too.
The mistake is easy to make, because free-text answers look like qualitative material. They are made of words, after all. The difference lies in what happens next. In a standardised survey, free-text answers are coded into categories and counted, so you end up with frequencies again. In a qualitative study, the same text is interpreted, and the result is a structure rather than a distribution.
In practical terms: two comment boxes at the end of a questionnaire do not turn your work into a mixed-methods study. For when each answer format earns its place, see our comparison of open and closed questions.
⚠️ Careful
Calling a standardised survey a “qualitative study” because it contains a few open questions is the easiest point for a marker to attack. Name the degree of standardisation instead, and explain how you coded the free-text answers.
How the ONS combined both approaches for Census 2021
The clearest British illustration of both approaches working side by side comes from census question development. The Office for National Statistics did not choose one method over the other, it used each for a different job.
Its overview of question and questionnaire development for Census 2021 sets out both toolkits in separate tables and records that testing ran “between 2016 and 2020”. The ONS states that “all testing followed a basic structure, beginning with engagement with data users to understand their requirements, followed by a programme of qualitative, quantitative and user experience (UX) testing”.
The order is the point. Qualitative work such as in-depth and cognitive interviews found out whether people understood a question at all. Quantitative testing then measured how the wording performed at scale. One more detail from the same source is worth borrowing for your own limitations section: the ONS describes an online panel as “a self-selected group of research participants who are willing to take part in research over an extended period”. Self-selected is not random, and our article on representative surveys explains what follows from that.
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How many participants does each approach need?
Qualitative studies work with few cases, quantitative surveys need enough cases for the analysis you have planned. The number therefore follows the method, and that reasoning belongs in your methodology chapter rather than a round figure pulled from nowhere.
For the qualitative side there is a solid reference point. Guest, Bunce and Johnson analysed sixty in-depth interviews with women in two West African countries in the journal Field Methods in 2006 and tracked the point at which no new themes appeared. Saturation occurred within the first twelve interviews, and the basic elements of the metathemes were present after six. For a homogeneous group and a narrow topic, a number in the low twenties is defensible rather than a compromise.
On the quantitative side no equivalent rule of thumb exists, because the required sample size depends on the analysis and the precision you want. How you recruit matters more than how many you reach. People who click your link in a group chat or on social media are a self-selected sample, not a random one, so your findings describe the people who answered rather than “students” or “customers” in general. Say so plainly in your limitations.
When it makes sense to combine both approaches
Qualitative and quantitative work combine well when each part has its own job and the two run in sequence. Placing them side by side inside a single questionnaire almost always produces a standardised questionnaire with comment boxes, nothing more.
The usual order is qualitative first. You hold six to ten open conversations to find out which themes matter at all, then build the questionnaire that measures how widespread those themes are. The reverse works just as well: an online survey throws up an unexpected result, and a handful of interviews explain why. Census 2021 followed exactly this logic, with qualitative testing preceding quantitative testing.
Either way each part keeps its own sampling, its own data collection and its own analysis. That is the requirement the quick version with an extra text box cannot meet. For how analysis differs by data type, see our overview of evaluation methods in empirical research.
A comment box does not make a qualitative study. Two data collections with two analyses do.
Common mistakes when classifying a survey
Most classification errors do not come from ignorance. They come from writing the methodology chapter after the survey has already gone out, so the label has to fit whatever happened. A lean standardised survey then turns into a “qualitative pilot study”.
Three patterns come up again and again. All three take minutes to avoid before you send the survey, and a second round of data collection to fix afterwards.
Mistake 1: confusing the answer format with the approach
Open text fields look to many students like proof of qualitative work. The approach depends on the procedure, not on the input box. A standardised questionnaire with three free-text questions is still a quantitative survey whose comments get coded. The consequence is very concrete: the methodology chapter names a method that never appears in the results.
Mistake 2: relabelling a low response rate as qualitative
When eighteen replies arrive instead of the two hundred you hoped for, “qualitative study” sounds like an elegant rescue. It is not one. A qualitative study differs by open data collection and interpretive analysis, not by having fewer cases. Naming the low response rate as a limitation is both more honest and academically stronger.
Mistake 3: generalising further than the sample allows
A survey among your own coursemates describes your own coursemates. The ONS draws census samples from address registers; a link posted in a student group reaches whoever had a spare minute. Describe your recruitment precisely and derive the reach of your claims from it. For the rest of the build, see our tips for online surveys.
Conclusion
In the end it comes down to one question: before data collection began, was it fixed what would be asked, in which order and with which answer options? If yes, your study is quantitative, and open follow-up questions do not change that. Anyone who genuinely wants to work qualitatively plans a separate data collection with its own analysis.
Where to go next
- Want the terms themselves? Qualitative and quantitative compared
- Planning a standardised survey? Quantitative surveys explained
- Planning open interviews? Qualitative surveys explained
- Running a survey for your dissertation? Surveys for your dissertation
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