Teams might assume that simply sprinkling in a quantitative survey alongside interviews qualifies as mixed-methods research. In reality, effective mixed-methods research involves more than just ensuring that both qualitative and quantitative methods appear somewhere in the same project.

What Is Mixed-Methods Research?

In the context of UX work:

Mixed-methods research combines qualitative and quantitative methods within a single research project to answer the same overarching research question.

Rather than treating qualitative and quantitative approaches as separate tools used in isolation, mixed-methods research intentionally integrates them before, during, and after data collection to provide a holistic view of the user experience.

Quantitative methods show you measurable patterns and trends at scale, while qualitative methods give you context and reveal the why — the motivations, frustrations, and mental models. By combining both, you gain not just numbers or stories in isolation, but a layered understanding that connects what’s happening with why it’s happening.

The term “mixed-methods research” can sometimes be used to describe studies where one method is added later in response to unexpected findings or new questions that arise mid-project. While that flexible approach can be useful, for the purposes of this article, we focus on mixed-methods research that’s intentionally designed from the start — where qualitative and quantitative methods are planned together, aligned under the same goal, and analyzed in relation to one another.

This intentional upfront planning is important for ensuring that both types of data contribute meaningfully to answering the same research question, rather than producing disconnected or redundant insights.

But even if you plan in advance to have both ways of data collection in your study, the specific details of the protocol — such as the interview questions or the tasks — may evolve along the way based on what you find.

Example of a Mixed-Methods Project

Imagine you and your team are planning a major redesign of a hotel website.

You plan to start with a quantitative benchmarking study to measure task success and completion tasks for top tasks, such as booking a hotel room, locating the checkin and checkout times, or finding the reservation-cancellation policy. (Once the redesign project is complete, you will collect these same metrics with the new design to look for improvements.)

From the beginning, you also plan to conduct a qualitative usability test afterward, but the exact tasks that will be used in the qualitative part won’t be fully defined up front.

Instead, you decide that the qualitative portion will focus on a subset of the tasks from the benchmarking study — specifically, the tasks where users struggled the most. This will allow you to focus particularly on uncovering the reasons behind those struggles and gathering in-depth insights to inform design improvements.

In this example, the quantitative results will guide the task selection of the qualitative usability test. But utilizing both methods of data collection were planned from the start under the same research goal: to understand where and why users struggle.

Why Use Mixed-Methods?

Balance Methodological Strengths and Weaknesses

Relying on only quantitative or only qualitative research leaves blind spots. Quantitative methods reveal patterns and trends across large groups, but they can’t always explain the “why” behind them. Qualitative methods uncover user motivations, frustrations, and mental models — but usually from smaller samples, which may not always generalize to the broader user population.

Get the Full Picture

By combining qualitative and quantitative methods, mixed-methods research delivers both scale and depth: measurable patterns alongside rich context. This layered understanding helps teams not only spot what’s happening but also grasp why it’s happening, which is key for making better-informed product decisions. Whether you’re shaping a product roadmap, refining a design, or prioritizing features, insights from mixed-methods research offer a more complete foundation for user-centered choices.

Inform the Next Phase of Research

Sometimes one method can be used to help with planning or refining the other. For example, early qualitative research can uncover users’ mental models, pain points, or language, which can then be used to write more targeted survey questions or choose meaningful tasks for a benchmarking study. Conversely, quantitative findings might highlight patterns or outliers that point to areas needing deeper exploration through user interviews or qualitative usability testing. In these cases, the insights from one phase directly inform the design of the next, making the overall research process more focused and impactful.

Important Considerations for Mixed-Methods Research

While mixed-methods research offers a comprehensive view of the user experience, it also comes with tradeoffs. When planning a mixed-methods project, keep in mind that more time and resources are required.

Running both qualitative and quantitative studies might mean managing two or more distinct protocols, participant groups, and data types.

For example, let’s say you’re in the discovery phase and plan on conducting both a quantitative survey and user interviews. You might need at least 40 participants for the quantitative survey and about 10 participants (depending on when saturation is reached) for in-depth user interviews. This all adds up to longer timelines, larger recruitment efforts, and closer coordination between researchers, designers, and stakeholders to align on objectives and findings.

3 Common Mixed-Methods Research Designs

Mixed-methods research can be designed in different ways depending on your research goals, timing, and available resources.

In UX, there are 3 common mixed-methods research designs:

• Explanatory sequential
• Exploratory sequential
• Convergent parallel

Each one connects qualitative and quantitative methods differently, offering a unique path to understanding your users.

Explanatory Sequential Design (Quant, Then Qual)

In an explanatory sequential design, you begin with quantitative research to identify trends or numerically measure performance. Then, you use qualitative research to explain or explore those quantitative findings in more depth.

Use this type of design when you anticipate that your quantitative results might raise questions that numbers alone can’t answer. For example, you plan on conducting an A/B test to compare two design versions. While the results will tell you which version performs better (or if there’s no meaningful difference), the numbers alone won’t explain why. So, from the start, you plan to follow up with qualitative usability testing to uncover the reasons behind user preferences, struggles, or lack of clear differences between the designs.

Explanatory sequential design can also help when you anticipate finding differences or patterns in your data and want to understand the reasons behind them. For instance, you plan to run a quantitative usability test with participants who have varying experience levels, specifically novice versus expert users. You anticipate that there will be noticeable disparities in their average time on task and success rates for certain key tasks. To understand the reasons behind those differences, you plan from the start to follow up with qualitative usability testing.

Exploratory Sequential Design (Qual, Then Quant)

In an exploratory sequential design, you begin with qualitative research to explore a topic or generate hypotheses. Then, you follow up with quantitative research to test, measure, or validate what you learned in the qualitative phase.Use this type of design when the topic is unfamiliar or loosely defined. For example, during the early discovery phase, you might conduct contextual inquiry with a small group to understand users’ context, behaviors, and needs. Based on those findings, you would then design and distribute a survey to a larger sample size to quantify and understand the generalizability of preferences and attitudes identified in the qualitative phase.

Also consider exploratory sequential design when you want to generate hypotheses before testing them at scale. For instance, imagine your goal is to make it easier for users to navigate your website. Your plan might be to start with qualitative usability testing to reveal where users expect to find content and how they interpret category labels. Next, you’d use those insights to design several options for a navigation schema. Then, you’d test those options at scale by conducting quantitative tree testing to identify which option performs best.

Convergent Parallel Design (Qual and Quant Simultaneously)

In a convergent parallel design, you conduct qualitative and quantitative research at the same time in parallel, then analyze the results together. This approach is efficient and allows you to compare and contrast qualitative and quantitative findings simultaneously.

Use this type of design when your study specifics allow for the qualitative and quantitative components to be conducted independently – without one being shaped by the results or design of the other – yet both add complementary evidence to the same research goal.

For instance, imagine your team needs to decide which features to prioritize. You might launch a quantitative MaxDiff survey asking users to prioritize features such as biometric login, instant transfer notifications, in-app chat with support agents, and so on. (A MaxDiff survey presents respondents with sets of options — such as product features — and asks them to choose the most and least important item in each set.) At the same time, you conduct interviews asking participants about their feature preferences and their reasoning behind their choices, which will uncover motivations and nuances that MaxDiff rankings can’t capture. Because of time constraints, you can’t afford to wait for one method to finish before starting the other — but using both concurrently ensures you capture both the what and the why behind user preferences. During analysis, you compare the findings to identify both similarities and differences.

Tips for Doing Mixed-Methods Research Well

To implement effective mixed-methods research, follow these three tips.

Plan Ahead

Mixed-methods research requires thoughtful coordination — especially with timing. If you’re selecting survey respondents for followup interviews, you’ll need to schedule enough time to review their survey responses before interviews begin.

That way, you can tailor your interview questions to explore specific patterns or outliers from the survey, rather than asking overly general questions.

Align Methods to Research Goals

Use each method for what it’s best suited to. For example, use A/B testing to quantify which design variant leads to higher conversion rates, and plan to follow up with qualitative usability testing to explore why users prefer one variant over another or where they get stuck.

Don’t run both methods just for the sake of variety – this could lead to a waste of time and resources. Use them intentionally to answer different facets of the same research question, such as understanding both the size of an effect and the reasons behind it.

Think Integration, Not Just Addition

The value of mixed methods isn’t just in having more data — it’s in how the data work together.

Ask yourself:

• How will the results connect?
• Will one method explain or inform the other?
• Are you looking to triangulate your findings from different angles?

For example, if your survey shows users are dissatisfied with onboarding, and your qualitative usability test reveals repeated confusion on the onboarding screen, the two methods reinforce and validate each other — enabling triangulation. That’s more compelling than either one alone.

Conclusion

Mixed-methods research isn’t about doing more for the sake of it. It’s about doing both qualitative and quantitative research in a deliberate, coordinated way to create richer, more actionable insights. By thoughtfully combining scale with depth, patterns with context, and measurable trends with human stories, mixed-methods research helps teams make better informed decisions. Whether you’re designing a new product, refining a user flow, or understanding unmet needs, investing the time to plan and integrate both methods can lead to more confident, well-rounded findings that truly reflect your users’ experiences.

References

Leslie A. Curry, Harlan M. Krumholz, Alicia O’Cathain, Vicki L. Plano Clark, Emily Cherlin, and Elizabeth H. Bradley. 2013. Mixed Methods in Biomedical and Health Services Research. Circ: Cardiovascular Quality and Outcomes 6, 1 (January 2013), 119–123. https://doi.org/10.1161/circoutcomes.112.967885

Jeff Sauro. 2015. 3 Ways to Combine Quantitative and Qualitative Research. (April 2015). Retrieved July 21, 2025 from https://measuringu.com/mixing-methods/