Is a survey qualitative or quantitative?
A survey can be quantitative, qualitative or contain both kinds of data. Fixed response choices, counts and ratings usually support quantitative summaries. Open questions collect people’s own words and can support qualitative interpretation. The questionnaire’s purpose and the way you analyze the answers matter more than the label “survey.”
A survey with ratings and a comment box contains both structured and open-text data. That alone does not establish a complete mixed-methods research design. For that, plan how the quantitative and qualitative work will address the research question together, including their analysis and integration.
The NIH mixed-methods resources emphasize intentional integration of the two approaches. In an operational feedback survey, the same principle is useful: explain what each type of answer contributes and how you will bring the findings together.
What is the difference?
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| Aspect | Quantitative survey component | Qualitative survey component |
|---|---|---|
| Example | How easy was registration? Choose a labeled rating. | What, if anything, made registration difficult? |
| Response | A category or numerical value | The respondent’s own wording |
| Typical analysis | Counts, percentages, distributions or appropriate statistical analysis | Interpretation of topics, meaning and context |
| Useful contribution | Shows the pattern among observed responses. | Explores experiences and ideas that fixed choices may miss. |
| Limit | The measure and sample constrain what can be inferred. | The available text and response coverage constrain interpretation. |
Qualitative does not simply mean “why,” and quantitative does not mean “objective truth.” A comment can describe what happened without explaining its cause. A numerical rating reflects a person’s judgment within the question you designed.
Three survey examples
A mainly quantitative membership survey
A network asks which services members used this year, how frequently they participated and which event time they prefer. The analysis counts categories and compares appropriately defined groups. This can be useful without requiring an explanation after every answer.
A qualitative exploration of an unfamiliar experience
A team asks a small group to describe the steps they took to access a service, where they needed help and what they would change. The purpose is to understand experiences and improve the questions for later collection. A survey cannot probe every ambiguity as an interview can, so wording and context matter.
A combined registration survey
A team asks an ease rating and an optional question about what influenced it. It analyzes the rating distribution, reads the comments and checks where the two agree or raise different questions. The combined interpretation helps decide what to investigate before the next intake.
For question examples, use closed-ended questions and open-ended survey questions.
How do you decide which components you need?
Start with the decision and the evidence missing from current records. If you need to estimate a known preference, structured choices may be appropriate. If you do not understand the possible experiences, open questions or interviews may help define them. If both a distribution and contextual accounts matter, plan both components.
Do not add text boxes simply to make the survey appear more complete. Every question requires respondent effort and a plan for use. An optional follow-up may be enough for a short feedback survey; a deeper research question may need a separate qualitative phase.
Also consider what respondents can know. They can describe difficulty uploading a document. They may not know the technical cause. Ask about their experience, then investigate the system separately.
Bring the findings together deliberately
First analyze each component appropriately. Then compare the results around the same question or decision. A simple table can make the relationship visible.
Fictional example: 100 respondents rate registration, with 60 choosing easy or very easy. Forty of the 100 also leave comments. Twelve comments mention confusing document requirements.
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| Evidence | What it supports | What to do next |
|---|---|---|
| 60 of 100 ratings are easy or very easy. | A majority of valid respondents reported a relatively easy experience. | Inspect the remaining rating categories and group coverage. |
| 12 of 40 comments mention document requirements. | The issue appears in 30% of the comments. | Review the original wording and the instructions. |
| Some positive ratings also accompany document concerns. | A broadly positive experience can include a specific difficulty. | Improve the step without assuming every concern produces a low rating. |
The combined finding could be: “Many respondents found registration easy, while document instructions were a recurring concern in optional comments. The team will test those instructions before the next intake.” It should not be: “Document instructions caused 40% of members to struggle.” Neither the number nor that causal claim follows from the evidence.
For a more detailed integration approach, see mixed-method design. For coding the comments, use open-response analysis.
What if the numbers and comments disagree?
Do not force agreement. Check whether the two questions referred to the same experience and period, whether the same people answered both and whether the interpretation was too broad.
A person may rate the overall service positively while criticizing one step. An optional comment set may contain a larger proportion of strong experiences than the full response set. A broad rating may miss a detail that matters to a smaller group.
Treat the difference as a question for review. Revisit source answers, inspect subgroup coverage and seek additional evidence where needed. Contradictory findings can sharpen the decision rather than invalidate the whole survey.
Does counting themes make text quantitative?
You can code text into categories and count how often they appear. The resulting counts are quantitative summaries of your coding decisions. The interpretation used to create those categories and understand the original accounts still matters.
State whether you count people, responses or mentions. One comment may contain several themes, so percentages can overlap. A theme in 12 comments is not necessarily 12 different people if someone could submit more than once.
Do not treat every comment as a full account of everything the respondent experienced. Not mentioning an issue does not prove its absence. The question’s wording and the space or effort available shape what people choose to write.
Do the answers need to be tied to a person?
They need an appropriate relationship, not always a personal identity. Within one questionnaire, a submission identifier can keep the rating and comment together anonymously. For a group comparison, suitable group and period fields may be sufficient.
Following the same person across waves requires an appropriate linking method and governance. If you collect from different people each time, label the comparison as group-level and inspect population changes.
Across a network, preserve local wording and a small shared core where comparison is needed. A local question about transport barriers and another about overall satisfaction should not be pooled as though they ask the same thing. A data dictionary helps contributors and analysts understand those boundaries.
Review AI-assisted interpretation
AI can help organize comments and suggest categories, but the team remains responsible for definitions and conclusions. Test ambiguous text, multiple topics and the languages collected. Keep the original response available and distinguish proposed coding from reviewed coding.
A generated explanation of a rating can sound convincing while adding a reason the respondent never supplied. Require source support and preserve uncertainty. For recurring analysis, record versions and review whether changed questions or categories alter the comparison.
Plan collection, analysis and governance together
Sopact’s relevant approach is to keep collected evidence, contextual analysis and review within a workflow the team can manage as sources and periods grow. Test whether a reviewer can inspect a rating and its comment, check a theme against the original text and understand the definition behind an aggregate.
Other survey tools can also store both response types. Evaluate the maintenance of your complete process: local questions, shared measures, imports, corrections, access and reporting. The benefit should be demonstrated with the work your team needs to do, rather than assumed from a product category.
How Sopact reduces coding and reporting work
The advantage of a combined survey should continue after collection: the coded response, rating and relevant context stay connected as the analysis develops.
A workflow with repeated manual work
- Define from an initial sampleRead material and agree on the codebook.
- Apply it across the datasetCode responses and check the result.
- Revise a definitionReturn to affected material and recode it.
- Reconnect the numbersReconcile coded results with ratings and context, then rebuild the view.
The Sopact workflow
- Your team owns the definitionsDecide what each code means and improve it as you learn.
- Apply coding across the eligible dataAutomate application; people review quality and exceptions.
- Reprocess after a definition changesReapply the revised definition across the configured scope instead of recoding each response by hand.
- Ask across coded text and numbersKeep the response, rating and relevant record context connected; inspect the evidence behind the result.
This compares workflow patterns, not a claim that every research tool requires manual coding or separate files. Some already automate parts of this work; compare the complete cycle.
For this codebook-based workflow, the main saving is repeated application and reconnection—not the removal of human judgment. A changed definition can be reapplied across the configured data while reviewers concentrate on quality, exceptions and interpretation. Coded text stays connected to the relevant ratings and context.
Count the recurring work in ownership cost. Include setup, coding, recoding after revisions, source reconciliation, review and reporting, plus your actual platform and processing expenses. A worked scenario of four cycles of 4,000 responses illustrates 272 fewer annual staff hours; it is an assumption-based example, not a customer benchmark. Existing automation, review needs and implementation effort can substantially change the result.
Adjust the workload assumptions and compare total effort →
A reliable assistant should calculate from the selected records and let a reviewer open the supporting evidence. Check the data scope, definition, denominator and access permissions. Reproducible arithmetic does not make every AI interpretation correct.
Watch: Why Qualitative Analysis Stays Small — And How to Scale It
See why revising a codebook creates repeat work, and how connected coding and quantitative analysis change that workload.
Watch the combined-analysis companion
See Sopact’s discussion of reviewing qualitative feedback with its context.
Frequently asked questions
Are surveys always quantitative?
No. Surveys can collect structured answers, open text or both. The purpose and analysis determine how the data is used.
Does adding a comment box create a mixed-methods study?
Not by itself. A mixed-methods design intentionally connects the quantitative and qualitative components to answer the research question.
Can I calculate percentages from qualitative responses?
Yes, after defining and reviewing the coding. Explain the unit and denominator, and retain the interpretation and source context behind the counts.
Must numbers and comments agree?
No. Differences may reveal distinct experiences, question scope or response coverage. Investigate them rather than selecting whichever result fits the preferred story.
Can an anonymous survey combine ratings and comments?
Yes. Both can remain linked within one submission without collecting personal identity. Take care that the comment itself does not reveal identifying details unnecessarily.
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