Qualitative vs quantitative research

Knowing when to use qualitative vs quantitative research helps you make informed decisions about your research approach. 

Whether you're validating a new product concept, understanding user behavior, or measuring the impact of design changes, choosing the right research method can make the difference between actionable insights and misleading data.

Many teams face a critical question: should you focus on understanding the "why" behind user behavior through qualitative research, or measure the "what" and "how much" through quantitative methods?

The answer isn't always straightforward. Each approach offers unique strengths. 

Qualitative research excels at uncovering user motivations and context, while quantitative research provides measurable insights that validate hypotheses and track performance. 

This guide explores both approaches in depth, helping you understand when to use each method and how to combine them for comprehensive insights.

Key takeaways

• Qualitative research explores "why." Uncover user motivations, pain points, and the context behind behaviors through in-depth insights from smaller groups of 5–30 participants.

• Quantitative research measures "what" and "how much." Gather statistical evidence and scalable data from larger samples (40+ for usability studies, 100+ for surveys) that can be generalized across populations.

• Choose your method based on your research goals. Use qualitative for exploring problems and generating ideas; use quantitative for testing hypotheses and measuring performance.

• Mixed-methods research provides the most complete picture. Combine both approaches to validate insights, reduce bias, and get both depth and breadth of understanding.

• Each method has trade-offs. Qualitative offers rich insights but limited generalizability; quantitative provides statistical rigor but may miss contextual factors.

• Lyssna supports both approaches. Run interviews and open-ended tests for qualitative insights, or use surveys and preference tests for quantitative data.

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What is qualitative research?

Qualitative research focuses on understanding the deeper context, motivations, and experiences behind user behavior. Rather than measuring what happens, qualitative research explores why it happens and what it means to the people involved.

Definition and overview

Qualitative research seeks to understand human behavior, motivations, and experiences through non-numerical data. It emphasizes depth over breadth, providing rich, contextual insights that help teams understand the "why" behind user actions.

This approach uses smaller sample sizes but generates detailed, nuanced data that reveals patterns in user thinking, emotional responses, and decision-making processes. 

Qualitative research is particularly valuable in the early stages of product development when teams need to understand user needs, identify pain points, and explore new opportunities.

Depth over numbers: The strength of qualitative research lies in its ability to capture the complexity of human behavior. 

Unlike quantitative methods that reduce behavior to numbers, qualitative research preserves the context and meaning that drive user decisions. This makes it essential for understanding user motivations, uncovering unmet needs, and identifying opportunities for innovation.

Key characteristics of qualitative research:

• Exploratory nature. Designed to discover new insights rather than test predetermined hypotheses, qualitative research helps you identify what you don't know you don't know.

• Contextual understanding. Captures the environment and circumstances that influence behavior, giving you the full picture of how users interact with your product.

• Flexible methodology. Can adapt during the research process based on emerging insights, allowing you to follow interesting threads as they appear.

• Rich, detailed data. Provides depth and nuance that numbers alone cannot convey, helping you understand the emotions and reasoning behind user actions.

• Human-centered approach. Focuses on understanding people as individuals with unique perspectives, building genuine empathy within your team.

Types of qualitative research

Qualitative research encompasses various methodologies, each suited to different research goals and contexts. 

For a comprehensive overview, see our guide to UX research methods.

User interviews are one-on-one conversations that explore user experiences, motivations, and pain points in depth. These sessions typically last 30–60 minutes and allow researchers to ask follow-up questions and dive deeper into interesting responses.

Focus groups bring together 6–8 participants to discuss a topic, product, or concept. The group dynamic can reveal insights that might not emerge in individual interviews, as participants build on each other's ideas and reactions.

Usability testing observes users as they attempt to complete tasks with a product or prototype. While it can generate quantitative metrics, the qualitative insights from user comments and observations are often the most valuable outcomes.

Diary studies ask participants to document their experiences, thoughts, and behaviors over an extended period. This longitudinal approach captures how experiences change over time and provides insights into long-term usage patterns.

Card sorting helps you understand how users categorize and organize information. Participants group related concepts, revealing their mental models and expectations for information architecture.

Example qualitative research questions

Effective qualitative research questions are open-ended and designed to encourage detailed, thoughtful responses. Here are examples that demonstrate how to frame questions for different research goals.

Understanding motivations:

• "What motivates you to upgrade to a paid plan instead of staying on the free version?"

• "Tell me about a time when you felt frustrated using our platform. What happened?"

• "What would need to change for you to recommend this product to a colleague?"

Exploring pain points:

• "What challenges do you face when trying to schedule a meeting using our platform?"

• "Walk me through your typical workflow. Where do you get stuck or feel confused?"

• "What's the most time-consuming part of your current process?"

Discovering user needs:

• "How do you currently solve this problem without our product?"

• "What would your ideal solution look like?"

• "What information do you need to feel confident making this decision?"

Understanding context:

• "Describe your workspace and how you typically use this type of tool."

• "Who else is involved in this process, and how do they influence your decisions?"

These questions encourage participants to share stories, provide context, and reveal the underlying factors that influence their behavior. The key is to ask "how" and "why" questions that prompt detailed explanations rather than simple yes/no responses.

Pro tip: Avoid leading questions that suggest a "right" answer. Instead of asking "Don't you find the checkout process frustrating?" try "How would you describe your experience with the checkout process?"

What is quantitative research?

Quantitative research focuses on measuring and analyzing numerical data to identify patterns, test hypotheses, and make statistical inferences about user behavior. This approach emphasizes objectivity, reproducibility, and the ability to generalize findings across larger populations.

Definition and overview

Quantitative research collects and analyzes numerical data to understand phenomena, test relationships between variables, and measure the extent of specific behaviors or attitudes. Unlike qualitative research, which explores the "why" behind behavior, quantitative research focuses on the "what," "how much," and "how often."

This approach uses structured data collection methods that can be easily measured, compared, and analyzed statistically. The goal is to quantify behaviors, opinions, or other defined variables and generalize results from a sample to a larger population.

Numbers that prove it: Quantitative research excels at providing concrete evidence for decision-making. When you need to know how many users complete a specific task, which design performs better, or whether a change has a statistically significant impact, quantitative methods provide the measurable data necessary for confident conclusions.

Key characteristics of quantitative research:

• Measurable data. Focuses on numerical information that can be statistically analyzed, giving you concrete numbers to support decisions.

• Larger sample sizes. Typically requires more participants to achieve statistical significance, with 40+ recommended for usability studies.

• Structured methodology. Uses standardized procedures that can be replicated, ensuring consistent and comparable results.

• Objective analysis. Minimizes researcher bias through statistical analysis, letting the data speak for itself.

• Generalizable results. Findings can often be applied to broader populations, helping you understand how your full user base will behave.

The strength of quantitative research lies in its ability to provide definitive answers to specific questions. For example, you might launch a survey asking 1,000 users, "On a scale of 1 to 10, how easy is it to complete a purchase on our site?" If 75% of users rate it 8 or higher, you know your checkout process is on track.

Types of quantitative research

Quantitative research methods vary in their approach to data collection and analysis, each serving different research objectives and providing unique types of insights.

Surveys are structured questionnaires that collect standardized information from many participants. They're excellent for measuring attitudes, preferences, and self-reported behaviors across large populations.

A/B testing compares two or more versions of a design, feature, or experience to determine which performs better. Preference testing helps you compare design options and gather user feedback on competing approaches.

Analytics analysis examines user behavior data from websites, apps, or other digital products. This includes metrics like page views, conversion rates, time on site, and user flow patterns.

First click testing measures task completion rates, time to click, and whether users can intuitively find their way through your interface. Research shows that if the first click is correct, users have an 87% chance of completing the action correctly.

Example quantitative research questions

Quantitative research questions are designed to generate measurable, numerical responses that can be statistically analyzed.

Measuring satisfaction and performance:

• "On a scale of 1 to 10, how satisfied are you with the checkout process?"

• "How many times per week do you use this feature?"

• "What percentage of your tasks can you complete without assistance?"

Comparing options:

• "Which of these three designs do you prefer?" (with specific options)

• "Rank these features in order of importance to you."

• "How likely are you to recommend this product to a colleague?" (Net Promoter Score)

Tracking behavior:

• "How long did it take you to complete this task?"

• "How many errors did you encounter during the process?"

Testing hypotheses:

• "Does the new design increase task completion rates?"

• "Are users more likely to upgrade when they see social proof?"

• "Which call-to-action button generates more clicks?"

These questions generate data that can be aggregated, compared, and analyzed to identify patterns and trends across large groups of users.

Pro tip: When designing rating scales, use consistent anchors throughout your survey. If 1 means "Not at all satisfied" in one question, don't flip it to mean "Extremely satisfied" in another.

Key differences between qualitative and quantitative research

Knowing the differences between qualitative and quantitative research helps you choose the right approach for your specific research goals and ensures you're collecting the most valuable data for your decisions.

Purpose and data types

The primary distinction between qualitative and quantitative research lies in their core purpose and the type of data they generate.

Qualitative research purpose:

• Explores and understands the meaning behind behaviors

• Discovers new insights and generates hypotheses

• Provides context and depth to user experiences

• Uncovers motivations, emotions, and thought processes

• Identifies opportunities for innovation and improvement

Quantitative research purpose:

• Measures and quantifies specific behaviors or attitudes

• Tests hypotheses and validates assumptions

• Provides statistical evidence for decision-making

• Tracks performance and identifies trends over time

• Generalizes findings to larger populations

The choice between data types depends on what you need to learn. 

If you're trying to understand why users abandon their shopping carts, qualitative research will reveal the underlying reasons, frustrations, and context. On the other hand, if you need to know how many users abandon their carts and at which step, quantitative research provides the measurable data you need.

Methodologies and sample sizes

The methodological approaches and sample size requirements differ significantly between qualitative and quantitative research, reflecting their different goals.

• Qualitative methodologies tend to be flexible and emergent, allowing the research design to evolve based on initial findings. Researchers can adjust questions and focus areas during data collection, emphasizing understanding the environment and circumstances while allowing participants to guide the conversation.

• Quantitative methodologies are structured and standardized, with consistent procedures that ensure reliable, comparable data. The research plan is established before data collection begins, with controlled variables that minimize external factors.

Sample size considerations:

Nielsen Norman Group's research shows that testing just five users for qualitative usability studies typically uncovers about 85% of usability problems. This smaller sample size works because qualitative research focuses on understanding patterns in user thinking rather than achieving statistical significance.

However, this guideline comes with important caveats. 

The 85% figure assumes a 31% problem discovery rate and applies specifically to qualitative usability testing with a homogeneous user group. For products with diverse user segments, you'll need to test 5 users from each distinct group.

Pro tip: For quantitative usability studies, Nielsen Norman Group recommends 40 participants as a starting point. This provides enough data for a reasonable margin of error while keeping studies manageable.

Outcomes and typical use cases

The outcomes and applications of qualitative and quantitative research align with their different strengths and data types, making each approach valuable for specific research scenarios.

The most effective research strategies often combine both approaches. 

If you notice a drop-off at the payment step (quantitative data), you might run follow-up interviews with users to figure out why they're leaving (qualitative insights). 

This combination provides both the scale to understand the magnitude of issues and the depth to understand their root causes.

Pros and cons of qualitative vs quantitative approaches

Every research method has strengths and limitations. Understanding these trade-offs helps you choose the most appropriate approach for your specific research goals and constraints.

Strengths of qualitative research

• Deep contextual understanding. Qualitative research reveals the "why" behind user behavior, providing rich context that numbers alone cannot capture. When users struggle with a feature, qualitative research uncovers whether the issue stems from unclear labeling, poor information architecture, or unmet expectations.

• Flexibility and adaptability. Research can evolve based on emerging insights. If an interview reveals an unexpected user need, researchers can explore this discovery immediately rather than waiting for a future study. This adaptability often leads to breakthrough insights that weren't anticipated in the original research plan.

• Uncovers hidden insights. Qualitative methods excel at discovering unknown problems and opportunities. Users often can't articulate their needs directly, but skilled researchers can identify patterns in their stories and behaviors that reveal underlying issues.

• Builds empathy and understanding. Hearing directly from users helps teams develop genuine empathy. These personal connections often motivate teams to prioritize user needs and create more human-centered solutions.

• Cost-effective for exploration. With smaller sample sizes, qualitative research can be more cost-effective for exploratory research, especially when you're not sure what questions to ask or what problems to solve.

Limitations of qualitative research

• Limited generalizability. Small sample sizes mean findings may not represent the broader user population. What's true for 10 interview participants may not apply to thousands of users, especially across different demographics or use cases.

• Potential for researcher bias. Qualitative analysis involves interpretation, which can introduce researcher bias. Different researchers might draw different conclusions from the same data, and leading questions can influence participant responses.

• Time-intensive analysis. Analyzing qualitative data requires significant time and expertise. Transcribing interviews, coding responses, and identifying patterns is labor-intensive compared to statistical analysis of quantitative data.

• Difficulty measuring impact. Qualitative research struggles to quantify the business impact of findings. While it can identify problems, it can't easily measure how many users are affected or the potential revenue impact of solutions.

Strengths of quantitative research

• Statistical significance and reliability. Large sample sizes enable statistical analysis that can determine whether observed differences are meaningful or due to chance. This provides confidence in findings and supports data-driven decisions.

• Scalable and generalizable. Results from properly designed quantitative studies can be generalized to larger populations, making them valuable for understanding broad user behavior and market trends.

• Efficient data collection. Once designed, quantitative studies can collect data from many participants quickly and cost-effectively. Automated tools can gather and analyze data with minimal manual intervention.

• Supports A/B testing. Quantitative methods enable controlled experiments that can definitively determine which option performs better, supporting evidence-based design decisions.

• Facilitates benchmarking. Numerical data enables comparison with industry standards, competitor performance, or internal benchmarks, providing context for interpreting results.

Limitations of quantitative research

• Limited contextual understanding. Numbers tell you what happened but not why it happened. High bounce rates indicate a problem but don't reveal whether it's due to slow loading times, irrelevant content, or poor navigation.

• Requires predetermined questions. Quantitative research can only measure what you think to ask about. If you haven't anticipated the right questions, you might miss important insights or opportunities.

• May miss nuanced insights. Standardized questions and response options can oversimplify complex behaviors and attitudes. Important nuances in user experience might be lost in the aggregation of responses.

• Less effective for innovation. Quantitative research is better at validating existing concepts than discovering new opportunities. It's difficult to measure demand for something that doesn't yet exist.

When and how to combine qualitative and quantitative research

The most powerful research strategies often combine both qualitative and quantitative approaches, leveraging the strengths of each method to create comprehensive insights that drive better decisions.

How a mixed-methods approach works

A mixed-methods approach strategically combines qualitative and quantitative research to provide both depth and breadth of understanding. This integration can happen sequentially, where one method informs the other, or concurrently, where both methods explore different aspects of the same research question.

Sequential mixed methods is the most common approach, using one method to inform the design and focus of the other. Start with qualitative research by running user interviews or live website testing to identify potential issues. Then use quantitative research to check if these issues are widespread.

For example, qualitative interviews might reveal that users find a checkout process confusing. Subsequent quantitative analysis can measure how many users abandon at each step and which specific elements cause the most problems.

Concurrent mixed methods runs both approaches simultaneously to explore different dimensions of the same question. While quantitative surveys measure user satisfaction scores, qualitative interviews explore the reasons behind those ratings.

Benefits of mixed-methods research:

• Comprehensive understanding. Combines the depth of qualitative insights with the breadth of quantitative data.

• Validation and triangulation. Findings from one method can validate or challenge findings from another.

• Reduced bias. Multiple perspectives help minimize the limitations inherent in any single method.

• Actionable insights. Provides both the evidence needed for decisions and the context needed for implementation.

Practitioner insight “In my work, it's sometimes necessary to recruit from our membership, but when it's not, I love how straightforward Lyssna's recruitment process is! Also, the way you can create a "mashup" study using a combination of testing methods is amazing. I'm used to these kinds of tools offering "either/or" choices - e.g. either a tree test or a survey. Lyssna lets me create a mixed-methods study, which I love.” 
– Benjamin B., Product Designer

Choosing the right method for your research question

The choice between qualitative, quantitative, or mixed methods depends on your research objectives, available resources, and the type of decisions you need to make.

Use qualitative research when:

• Exploring new problem spaces or user needs

• Understanding the "why" behind user behavior

• Generating ideas for new features or improvements

• Building empathy and understanding within your team

• Working with limited resources for exploratory research

Use quantitative research when:

• Testing specific hypotheses or assumptions

• Measuring performance or tracking changes over time

• Comparing different options or designs

• Needing statistical evidence for decision-making

• Generalizing findings to larger populations

Use mixed methods when:

• Tackling complex research questions that require multiple perspectives

• Validating qualitative insights with quantitative evidence

• Exploring new areas while also measuring current performance

• Building comprehensive user understanding for major product decisions

As a general framework: during the early design phase, use qualitative research like interviews to understand user needs. 

During prototyping, run usability tests for a mix of qualitative and quantitative data. In the final validation phase, use recordings on unmoderated tests to confirm that the design works.

Practical strategy for combining both

A successful mixed-methods approach needs careful planning, clear objectives, and smart integration of different research activities.

Step 1: Define your research objectives. 

Start with clear questions about what you need to learn and what decisions the research will inform. This clarity helps determine which methods will provide the most valuable insights.

Step 2: Plan your research sequence. 

Decide whether to run methods sequentially or concurrently based on your timeline, resources, and research goals. Sequential approaches often provide more focused insights, while concurrent approaches can be more efficient.

Step 3: Design complementary studies. 

Ensure your qualitative and quantitative research components address different aspects of your research question. Avoid redundancy while ensuring the methods work together to provide comprehensive insights.

Step 4: Integrate data analysis. 

Plan how you'll combine insights from different methods. Look for patterns that appear across both qualitative and quantitative data, as well as contradictions that might reveal important nuances.

Step 5: Present integrated findings. Use quantitative data to show the scale of the problem (for example, "30% of users drop off at this step"). Add quotes or video clips to humanize it (for example, "One user said, 'I couldn't figure out where to enter my discount code.'").

Common mixed-methods workflows:

1. Qualitative → Quantitative: Interview users to identify pain points, then survey a larger group to measure prevalence.

2. Quantitative → Qualitative: Analyze usage data to identify problem areas, then interview users to understand causes.

3. Concurrent validation: Run usability tests while collecting task completion metrics and user feedback.

4. Longitudinal tracking: Combine regular quantitative metrics with periodic qualitative check-ins.

How Lyssna can help

Lyssna supports both qualitative and quantitative research methods, so you can gather user insights fast and make data-driven decisions with confidence.

Running qualitative studies with Lyssna

Lyssna makes it easy to conduct qualitative research, from user interviews to unmoderated testing sessions that capture rich, contextual insights.

Fast access to participants

With our research panel of 690K+ participants, you can target by demographics and psychographics. Most orders are fulfilled in under 30 minutes.

Built for collaboration

Record sessions, take notes, and share insights with your team. Built-in collaboration tools help stakeholders stay involved and ensure findings reach the right decision-makers.

Test early, test often

Run unmoderated studies where participants think aloud as they complete tasks. Test concepts, wireframes, and prototypes to gather feedback before investing in development.

Practitioner insight: “With other research tools, I've created full, personalized guides for my team on how to use them. But with Lyssna, it doesn't really make sense for me to create a guide. It's so easy to use.”
– Danielle Morris, UX Researcher & Ops specialist at Mercury

Running quantitative surveys and tests with Lyssna

Lyssna helps you gather statistically significant data from large participant groups, giving you the metrics needed for confident decisions.

Surveys with flexibility

Create detailed surveys with various question types, logic branching, and conditional logic: from simple satisfaction surveys to complex multi-section studies.

Preference and A/B testing

Run controlled experiments with preference testing to compare designs, messages, or concepts. Set up A/B tests and gather statistically significant results quickly.

Usability metrics

Collect quantitative data with first click testing, including task completion rates and time to completion. Use card sorting to understand how users categorize information, and tree testing to validate navigation structure.

Practitioner insight: “We used to spend days collecting the data we can now get in an hour with Lyssna. We're able to get a sneak preview of our campaigns' performance before they even go live.”
– Aaron Shishler, Copywriter Team Lead at monday.com

Using Lyssna's platform for mixed-methods research

Lyssna makes it easy to combine qualitative and quantitative methods in studies that provide both depth and breadth of insights.

Flexible and cost-effective

Run unlimited tests, surveys, and interview studies with low recruitment costs (just $1 per credit). Transition seamlessly between research methods within the same study or across projects.

Consistent participant pools

Recruit participants once and use them across multiple research activities. This helps ensure your qualitative and quantitative samples represent similar user populations, making findings easier to compare.

A typical mixed-methods workflow

1. Start with interviews to understand user needs and pain points

2. Create surveys based on interview insights to measure prevalence

3. Run usability tests to gather behavioral data and user feedback

4. Validate findings through follow-up interviews or additional quantitative testing
   

Practitioner insight: "A full-blown research project can take a lot of time and energy, but you can have meaningful early results from Lyssna in a single day. I think that's one of the best benefits I've seen: faster and better iteration."
– Alan Dennis, Product Design Manager at YNAB

Ready to combine both research approaches?

Test with our 690K+ participant panel and switch seamlessly between methods. Start your mixed-methods research with Lyssna.

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Diane Leyman

Senior Content Marketing Manager

Diane Leyman is the Senior Content Marketing Manager at Lyssna. She brings extensive experience in content strategy and management within the SaaS industry, along with editorial and content roles in publishing and the not-for-profit sector