What is Mixed Methods User Research? A Definition and Examples
“Mixed methods research combines qualitative and quantitative methods in a single study, so you get both the numbers and the reasons behind them. Qualitative research tells you why people behave as they do; quantitative research tells you how many and how much. Used together they cover each other’s blind spots.”
There are three standard designs: exploratory (qualitative first, then quantitative to measure what you found), explanatory (quantitative first, then qualitative to explain it), and convergent or dynamic (both at once, integrated). A classic example: interview customers to surface the problems they face, then run a ranking survey to measure which problem matters most. This is the qualitative-plus-quantitative backbone of modern product and UX research, and it's what the OpinionX Discovery Sandwich framework is built on.
What is mixed methods research?
Mixed methods research is an approach that combines qualitative and quantitative methods within one study or programme of research, integrating the two rather than running them in isolation.
The line between qualitative and quantitative research has blurred as research at scale has become common. Fast-growing companies increasingly prize researchers fluent in both, who can adapt to any research challenge rather than defaulting to one toolkit. It's now a field of expertise in its own right, with its own peer-reviewed journal and a large academic literature behind it.
For a long time the two were treated as rivals. Each camp claimed its methods mattered more to a company's success, and a researcher would run a study using one or the other, then interpret the findings alone.
Many professors believe researchers are still stuck with the false dichotomy of qual vs. quant. Since joining the team at dscout, I’ve come to understand this choice is a false one. New technologies enable researchers to combine the two seamlessly."
Carolyn Wahlen — Research Analyst, dscout
Where did mixed methods research come from?
The rules of user research changed as user experience became central to how companies compete. As product-led and lean startup strategies spread, founders took on user research themselves, then handed it to technically minded product managers as they grew.
Those product managers quickly learned that big data could tell them what users were doing but not why.
“Data can tell you the what but it can’t tell you the why. We use surveys to get to the why."
Anna Marie Clifton — Product Lead, Asana
Technical teams were already using quantitative research to justify their decisions, so qualitative research became the natural way to inform those quant studies. The combination caught on at scaling companies first, and it has since found its footing well beyond young startups.
What makes mixed methods research unique?
Quantitative research is what most product-led companies run on internally: hard data to measure, validate and settle arguments like "which feature do users like most?" or "which design wins?". What it can't do is tell you the reasons underneath those numbers.
Filling in those reasons is the qualitative half's job, and combining the two is a form of triangulation: several angles on one question give you a more reliable picture than any single angle can. Qualitative work surfaces the reasoning, the context, and the unknown unknowns you'd never have thought to ask about. What makes the approach distinctive is that it refuses to pick a side.
That's also why it pairs so naturally with methods that turn qualitative insight into quantitative data. Once interviews surface a set of problems, a stack ranking survey measures which matter most, turning quotes and problem statements into numbers you can act on.
Why do product leaders value these researchers?
A researcher who can move between qual and quant answers the whole question rather than handing half of it to someone else. They run the interviews that surface a problem and the survey that sizes it, without losing the thread between the two steps or waiting on a specialist to pick it up. For a product leader deciding where to point engineering time, that mix of depth and evidence is what makes a decision feel safe to commit to.
Why is it becoming more important?
Three shifts turned it from a nice-to-have into a core skillset. Research at scale made purely qualitative work look thin on evidence and purely quantitative work look blind to context. Better tooling made it far easier for non-specialists to run both. And product decisions now move fast enough that the slow hand-off between separate qual and quant studies has become a liability. We've made the fuller case in why mixed methods is the most important research skillset of the decade.
The 3 mixed methods designs
There are three core designs. They differ in the order you run the methods and how you combine them, and the right one depends on what you already know and what you're trying to find out.
| Design | Order | Use it when |
|---|---|---|
| Exploratory | Qualitative → quantitative | You don't yet know what to measure, so you explore first, then measure |
| Explanatory | Quantitative → qualitative | You have data showing a pattern and need to understand why it's happening |
| Convergent (dynamic) | Qualitative + quantitative at once | You want to collect and integrate both in a single step |
1. Exploratory: qualitative, then quantitative
Start with qualitative research to explore a space you don't yet understand, then use quantitative research to measure what you found. Interview customers to surface the problems they face, then run a ranking survey to see which problem is most widely and strongly felt. This is the most common design in product discovery, and it's the shape of the Discovery Sandwich: qualitative breadth, then quantitative prioritisation, then qualitative depth.
2. Explanatory: quantitative, then qualitative
Start with the numbers and use qualitative research to explain them. Your analytics show a drop-off at a particular step, or a survey flags an unexpectedly low score, and you run interviews to work out why. The data finds the pattern; the interviews tell you what's behind it.
3. Convergent (dynamic): both at once
Collect qualitative and quantitative data together and integrate them, rather than running one after the other. A survey that captures both a ranked preference and an open-text reason in the same question is a simple example. Tools built for this cut out the manual step of carrying insight from one method into the next. OpinionX works this way: participants rank options and the open-ended responses themselves get voted on, so qualitative input becomes quantitative data inside a single study.
Mixed methods research examples
Four to make the designs concrete.
Product roadmap (exploratory). Interview 15 customers to collect the problems they mention, then put the full list into a ranking survey to measure which problems your wider base cares about most.
Churn investigation (explanatory). Your data shows a spike in cancellations after month three. You interview churned users to find out what changed, turning a number into a reason you can act on.
Pricing research (convergent). Run a survey that captures willingness to pay and asks people to explain their answer in their own words, then analyse both together.
Feature feedback (convergent). Ask users to rank capabilities and, in the same study, vote on open-ended suggestions from other participants, so the qualitative ideas get quantitatively prioritised.
No single method carries the whole answer on its own, which is the point of running both.
How to become a full-stack researcher
The researchers product teams value most run the whole loop: find a problem qualitatively, size it quantitatively, then go back to qualitative work to understand it in depth. That's the full-stack researcher, and it's learnable rather than a talent you're born with. Our guide to running mixed methods research without being a quant expert is the practical starting point, and thematic analysis covers turning raw qualitative data into something you can quantify.
The academic field backs this up at far greater length: the Journal of Mixed Methods Research exists precisely because combining the two approaches produces insight neither delivers on its own.
Frequently asked questions
What is mixed methods research? Mixed methods research combines qualitative and quantitative methods within a single study, integrating the two so you get both the numbers and the reasons behind them. Qualitative research explains why; quantitative research measures how many and how much.
What are the three types of mixed methods research? Exploratory (qualitative first, then quantitative), explanatory (quantitative first, then qualitative), and convergent or dynamic (both collected and integrated at once). The right design depends on whether you're exploring, explaining, or doing both together.
What is an example of mixed methods research? Interviewing customers to surface the problems they face, then running a ranking survey to measure which problem matters most to your wider base. The interviews provide the qualitative depth; the survey provides the quantitative scale.
What's the difference between mixed methods and multimethod research? Multimethod research uses more than one method of the same type, two qualitative methods for instance. Mixed methods specifically combines qualitative and quantitative approaches, which is what makes it distinctive.
Why is mixed methods research important? Because numbers alone can't tell you why people behave as they do, and interviews alone can't tell you how widespread a finding is. Combining them covers each method's blind spot, which is why product and UX teams increasingly treat it as a core skillset.
Most of the best product research I've seen runs this way whether or not anyone calls it "mixed methods": a round of interviews to work out what's going on, a survey to find out how common it is, then back to the people for the detail. Run them in the order that fits what you're trying to learn, and you stop having to guess.
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OpinionX is built for the quantitative half of a mixed methods study: it turns qualitative insights like quotes and problem statements into ranked, quantitative data. It's free to start: $0, unlimited surveys, unlimited researcher seats, capped at 25 participants per survey, then $900 a year to lift the cap (full pricing). Create a ranking survey, or read the Discovery Sandwich framework first.