Jan 10,2026 
People ask this question all the time, and the honest answer is that it depends on how you build the survey. A survey can collect numbers. It can also collect stories. Some surveys do both at once. The tool itself does not pick sides. You do, when you write your questions.
This matters because researchers often get stuck thinking surveys belong in one category or another. They design their entire study around that assumption. Then they miss out on useful data because they limited themselves before they even started. Understanding how surveys work across both research types gives you more options and better results.
TL;DR
- Surveys can be quantitative, qualitative, or both depending on question design
- Quantitative surveys use closed-ended questions and produce numerical data for statistical analysis
- Qualitative surveys use open-ended questions and capture detailed reasoning and context
- Mixed methods research combining both approaches has grown from 0.64% to 2.97% usage between 2011 and 2024
- Respondent fatigue reduces data quality, with 54% to 71% of employees reporting survey fatigue
- Evelance helps augment rather than replace traditional qualitative methods
- Evelance Personas achieve 89.78% accuracy against real human responses, compressing research cycles while improving insight quality
How Quantitative Surveys Work
Quantitative surveys focus on numbers. They use closed-ended questions with set answer choices. Think rating scales, multiple choice, and yes or no options. Respondents pick from a list, and you end up with data you can count and measure.
This approach works well when you want to find patterns in large groups. If you need to know how many people prefer a certain feature, a quantitative survey gives you a percentage. You can run statistical analysis on the results. You can compare groups and identify trends.
The strength here is scale. You can reach hundreds or thousands of people and summarize their responses in clean numbers. Researchers use this method when they want to generalize findings to a larger population or test a specific hypothesis.
How Qualitative Surveys Work
Qualitative surveys take a different path. They rely on open-ended questions that let people respond in their own words. Instead of picking from a list, respondents write out their thoughts, feelings, and reasoning.
This approach captures detail. When someone explains why they abandoned a checkout process or what frustrated them about an onboarding flow, you get context that a rating scale cannot provide. You learn the reasoning behind decisions.
Research published in Frontiers in Research Metrics and Analytics in 2025 confirms that surveys are commonly associated with quantitative methods, yet there is growing recognition of their potential to yield qualitative insights into complex social phenomena. The method can flex based on how you design it.
The tradeoff is analysis time. Reading through open-ended responses takes longer than running numbers through a spreadsheet. You also cannot generalize as easily because the data is descriptive rather than statistical.
Mixed Methods: Using Both at Once
Here is where things get practical. Many researchers combine both question types in a single survey. You ask some closed-ended questions to capture measurable data, then include open-ended questions to understand the story behind those numbers.
This approach has become more common over time. Research examining educational journals shows that mixed methods research use has grown from 0.64% in 2011 to 2.97% in 2024. More researchers are recognizing that combining approaches produces stronger results than relying on one method alone.
A mixed methods survey might ask respondents to rate their satisfaction on a scale of 1 to 10, then follow up with a question asking them to explain their rating. The number tells you how they feel. The explanation tells you why.
This combination gives you breadth and depth. You can quantify patterns while also understanding the human reasons behind those patterns.
The Problem with Long Surveys
Surveys sound simple in theory. Write questions, send them out, collect responses. But getting quality data is harder than it looks.
Respondent fatigue represents a real problem. Long surveys with extensive follow-up questions, poor design, repetitive items, and too many open-ended prompts all contribute to people checking out mentally. When respondents get tired, they start skipping questions or giving low-effort answers.
The numbers on this are telling. An additional hour of survey time increases the probability that a respondent skips a question by 10% to 64%. Research from SHRM shows that 54% of employees report survey fatigue due to frequent feedback requests. A Harvard Business Review study found that 71% of employees feel fatigue from excessive survey frequency.
This affects data quality. Fatigued respondents give you worse answers, which means your results become less useful. The problem hits open-ended questions especially hard because they require more effort from respondents.
How AI Is Changing Survey Research
Researchers are exploring artificial intelligence to address some of these limitations. AI can support dynamic survey design, probe questions more effectively, and help keep participants engaged without exhausting them.
Many qualitative research scholars view AI as best suited to augment rather than replace traditional methods. The technology helps speed up analysis of open-ended responses. It can identify themes across hundreds of written answers faster than manual coding. But it works alongside human researchers rather than taking over their role.
This matters for teams doing user research. Traditional qualitative methods like focus groups and interviews uncover rich detail, but they take time and resources. Adding AI-assisted analysis lets teams process more data without sacrificing depth.
How Evelance Fits Into Modern User Research
Evelance takes this augmentation approach seriously. The platform does not replace user research. It accelerates and augments it by reducing research cycles, lowering costs, and saving time.
The way it works involves predictive personas built on behavioral science and AI. Evelance Personas achieve 89.78% accuracy when validated against real human responses. This accuracy rate comes from comparing predictive persona feedback with actual user research conducted on the same designs.
Teams use this to pre-validate designs before running live sessions. When live research explores designs that have already been tested through predictive personas, teams report finding 40% more insights. They spend their time on refinement rather than discovering fundamental problems.
The platform compresses research cycles from weeks to minutes while increasing the accuracy of design and product decisions. This makes user research more efficient and inclusive, helping teams reach validation faster with stronger, more focused designs.
Picking the Right Approach for Your Research
The question of whether surveys are qualitative or quantitative has a simple answer: they can be either or both. Your research goals should drive the decision.
If you need to measure how many people hold a certain opinion, go quantitative. If you need to understand why they hold that opinion, go qualitative. If you need both, build a mixed-methods survey that combines question types.
Keep surveys focused to avoid fatigue. Consider AI tools that can help with analysis and participant engagement. And remember that surveys work best as part of a larger research strategy that matches methods to questions.