qualitative and quantitative research ux

Qualitative vs. Quantitative UX Research—What’s the Difference?

The idea of user-centered products is a focal point across a variety of industries—tech-related or otherwise. Companies are seeing the benefits of placing their users at the forefront of their design decisions. But how do you know what your users’ needs actually are?

The best way to ensure a final product delivers on users’ needs is to conduct lots of user research throughout the design process. There are a variety of ways to conduct user research, but most methods fit into one of two categories: qualitative and quantitative user research.

While one is generally in no way better or more useful than the other, there are key differences that make qualitative data more useful at certain times than others—and vice versa. In this article, we’ll focus on the differences between these research methods as well as when and how to use each type.

We’ve broken down this guide to qualitative versus quantitative user research as follows:

• Quantitative vs. qualitative UX research
• More about quantitative UX research
• More about qualitative UX research
• When to conduct qualitative or quantitative user research
• Examples of qualitative and quantitative research methods
• Making qualitative and quantitative UX research work together
• Key takeaways

Let’s get started!

1. Quantitative vs. qualitative UX research

In short, quantitative user research is research that yields numerical results, while qualitative research results in data that you can’t as easily slot into a calculation. 

The type of research you conduct is very much reliant on what your research objectives are and what kind of data will best help you understand your users’ needs.

Our one, overarching piece of advice: Don’t underestimate either type of research. Both can offer invaluable insights that can guide your design process to incredible outcomes.

2. More about quantitative UX research

Let’s start with the numbers. What is quantitative UX research , what does it look like, and what are the benefits of conducting this type of user research?

Quantitative user research is the process of collecting and analyzing objective, measurable data from various types of user testing.

Quantitative data is almost always numerical and focuses on the statistical, mathematical, and computational analysis of data. As the name suggests, quantitative user research aims to produce results that are quantifiable.

Examples of quantitative data

Quantitative data answers questions of:

In UX design, analytics are a huge source of quantitative data. Page visits, bounce-rates, and conversion rates are all examples of quantitative data that can be gathered using analytics.

User testing sessions can also be great wellsprings for quantitative data. Task completion times, mouse clicks, the number of errors, and success rates are all forms of quantitative data that you can obtain by including some quantitative elements in your user testing.

Benefits of quantitative user research

Due to the objective nature of quantitative user research, the resulting data is less likely to have human bias as it’s harder to lead participants to a certain outcome and has well-defined, strict, and controlled study conditions.

Quantitative data is also often simple to collect, quicker to analyze, and easier to present in the form of pie charts, bar graphs, etc. Furthermore, clients may prefer to see hard statistics and find it easier to link them back to their KPIs as a way to justify investment for future improvements.

3. More about qualitative UX research

This leads us to our second type of research: qualitative user research . What is it exactly, and what are the benefits of incorporating it into your research process?

Qualitative user research is the process of collecting and analyzing non-numerical data in the form of opinions, comments, behaviors, feelings, or motivations. Qualitative data aims to give an in-depth look at human behavioral patterns.

Examples of qualitative data

Qualitative data cannot be as easily counted and funnelled into a calculation as it’s quantitative cousin. Where quantitative research often gives an overarching view, qualitative research takes a deeper dive into the why .

Qualitative research often takes the form of user surveys, interviews, and observations or heuristic analysis and focus groups. Just as with quantitative data, user testing sessions offer tons of opportunities to gather qualitative data.

Benefits of qualitative user research

Qualitative research gives a more in-depth look at your users and will often reveal things that quantitative data can’t. Qualitative testing employs a “think-aloud” approach that allows you to get inside the mind of the person using your product and see how they use it in their own environment and what sort of response they have to it.

Qualitative data helps you make accurate, informed choices for your users instead of guessing about causation. Obtaining this empathetic and emotionally-driven evidence may make it easier for stakeholders to invest in changes to the product.

4. When to conduct qualitative or quantitative user research

While qualitative user research can be conducted at any point in the design process, quantitative user research is best done on a final working product, either at the beginning or end of a design cycle. This is due to a few reasons, which we’ll cover in this section.

The goals of quantitative research are summative and evaluate metrics on an existing product or site. Companies often use quantitative research to evaluate if a redesign of a final product is needed, to track a product’s usability over a period of time, and compare a product with its competitors. It’s also used to calculate ROI (return of investment) in order to understand how efficient a product is at making an appreciable profit.

Conversely, qualitative user research is both formative and summative and is used to inform design decisions at any point in the design cycle, help ensure that you’re on the right track. Qualitative research identifies the main problems in design, pinpoints usability issues, and helps uncover possible solutions for them within the design process.

Furthermore, because quantitative user research usually involves large numbers of users (>30 participants), conducting quantitative usability tests too early or too often in the design process can be costly, whereas the more intimate and smaller qualitative testing (5-8 participants) is often more affordable and easier to justify.

5. Examples of qualitative and quantitative research methods

Here, we’ve listed some examples of qualitative research methods, quantitative research methods, and research methods that fit into both categories.

Qualitative

• User interviews
• Focus groups
• Diary studies
• Shadow sessions

Quantitative

• Funnel analysis
• Mouse or heat maps
• Cohort analysis

Both qualitative and quantitative

• A/B testing
• Card sorting
• Tree testing
• Storyboarding
• Visual affordance usability testing

6. Making qualitative and quantitative UX research work together

As you may have noticed, there are many research methods that render both quantitative and qualitative data. Furthermore, it’s uncommon for designers to run just one form of user research. This is because quantitative and qualitative user research data are best used together in order to obtain a more comprehensive idea of the issues at hand and their possible solutions.

Conducting both quantitative and qualitative research helps you form hypotheses as well as come up with the metrics on how to test it. Using just one type of research often leads you with unanswered questions and vague or false metrics. When used in conjunction, quantitative data will answer your “what, how many, and how much?” questions while qualitative data gives you the answers to “why?”

7. Key takeaways

Quantitative and qualitative user research are both necessary in the process of designing products and experiences that truly meet users’ actual needs and goals.

Quantitative research are larger tests that give a summative evaluation of the overall usability of an existing product and are always reported in numerical form through metrics like satisfaction ratings, task times, number of clicks, and bounce or conversion rates.

Qualitative user research are smaller sessions that give non-numerical, formative information as to what the main issues of usability issues of a product are and are reported as quotes, emotions, or observations.

While quantitative and qualitative user research methods have different goals, they are complementary to each other and give designers a fuller, more comprehensive idea of the success of their product design.

If you’d like to learn more about UX research, check out these articles:

• What is user research and what’s its purpose?
• How to conduct inclusive user research
• Top 5 UX research interview questions to be ready for
• 5 Mistakes to avoid with your UX research portfolio

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What is UX Research: The Ultimate Guide for UX Researchers

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Quantitative vs. qualitative UX research: An overview of UX research methods

UX research is a multi-dimensional process that includes different user research methods and techniques. In this chapter, we provide an overview of the quantitative and qualitative research methods and explain why the best solution uses a mix of both methods.

What are quantitative UX research methods?

Quantitative research is used to collect and analyze numerical data, identify patterns, make predictions, and generalize findings about a target audience or topic. The data is collected indirectly, either through a UX research tool that automatically records it, such as Google Analytics or Maze, or manually by measuring and analyzing UX metrics.

Qualitative UX research made easy

Explore the powers of both quantitative and qualitative research to discover new insights and test final solutions.

Here are some of the most popular quantitative research methods you can use to collect valuable quantitative data:

• Quantitative usability testing collects usability metrics like time on task, error rate, or success rate. You can use this information to keep an eye on your product's UX and make sure it improves over time.
• Web analytics (or app analytics) provides insights into what people actually do in your product. Analytics data can help you monitor your product's performance and identify problems.
• Card sorting is used to discover how people understand and categorize information. Analyzing the percentage of participants who grouped cards in a similar way can help you determine which categories would be understandable to most users.
• Surveys are a great way to gather information about your users' attitudes and behaviors. You can get qualitative data through open-ended feedback or quantitative data by tapping into a larger volume of responses.

Importance of quantitative research methods

Quantitative data provides a foundation for benchmarking and ROI calculations and can help you decide the best performing version of a design or product.

Quantitative UX researchers collect information by measuring actions, thoughts, or attitudes in different ways, such as conducting voluntary surveys and online polls or analyzing log data.

Duyen Mary Nguyen , Quantitative UX Researcher

Quantitative data aims to answer research questions such as ‘what,’ ‘where,’ or ‘when.’ For example, when collecting usability metrics such as task success rates, time on task, completion rates, clicks, conversion rates, and heatmaps, you can measure how well a design performs and spot issues on a page or in the user flow.

One of the advantages of quantitative research is the ability to run studies with large sample sizes and collect statistically relevant data. As opposed to qualitative feedback, which is interpretable by the researcher and subjective, quantitative research is more objective and representative of a broader audience.

I choose quantitative methods if I need to prioritize one solution over the possible alternatives or to validate an idea, wireframe, prototype or even MVP.

Yuliya Martinavichene , User Experience Researcher at Zinio

What are qualitative UX research methods?

Qualitative user research includes research methods like user interviews and field studies and helps you collect qualitative data through the direct observation and study of participants. Qualitative data yields an understanding of the motivations, thoughts, and attitudes of people. This type of research is key to uncovering the ‘why’ behind actions and develop a deep understanding of a topic or problem.

Yuliya Martinavichene , User Experience Researcher at Zinio, highlights: “Since researchers are curious folks, we prefer not only to observe what people are doing by looking at analytics but also to understand the “why” behind the user behavior.”

She compares the process of running a qualitative study to casting a wide lens to identify user behavioral patterns:

Qualitative research methods come into play when you need to discover, understand and empathize with users, and are not conducted only in the exploratory research phase, but iteratively, throughout the whole development process.

There are different qualitative research methods you can employ for your studies, such as user interviews, diary studies , focus groups, card sorting , usability testing , and more. We explore the most common UX research methods in the next chapter.

Choosing the right user research techniques depends on the project and your research goals. Yuliya explains:

In real-life, there is no “Oscar-winning” scenario and the best answer for the eternal question “What user experience research method should you use? is simply an unsatisfactory “It depends!” Different research pain points call for specific methods and approaches.

Yuliya collects qualitative feedback through different methods depending on the goals of the projects. For example, she might conduct walk-throughs with users and asks them to show her around the software she is researching to understand how they currently use the product. Or she may ask research participants to perform everyday tasks to observe their behavior in real-time, such as logging in or out of the platform.

To gather more qualitative insights, Yuliya also checks social media mentions, analyzes blog posts, and reads app store reviews to collect information about the experience users have with the product.

Qualitative research gives you rich insights about the people, product, and the problem you’re researching, and helps you inform decision-making throughout the design and product development process.

Quantitative vs. qualitative research methods

The key differences between quantitative and qualitative research are in the data they deal with and the questions they answer–where quantitative research focuses on numbers and statistics to answer ‘what’, ‘where’ and ‘when’, qualitative research broadly looks to words and meaning for the ‘why’.

Both methods have their merits, and likewise their drawbacks. As we go on to explore, for the most robust and meaningful research, it’s best to use a combination of quantitative and qualitative, but in certain situations, such as challenges due to time or resource constraints, you may decide to use one or the other.

Quantitative research:

• Answers the questions ‘what’, ‘where’ and ‘when’
• Provides a foundation for benchmarking and ROI calculations
• Allows for large sample sizes
• Analyzes numerical data, identifies patterns, makes predictions
• Collected indirectly through UX research tools or metrics

Qualitative research:

• Answers the question ‘why’
• Provides rich insights about the people, the product and the problem
• Allows tight focus on small sample sizes
• Develops a deep understanding of the topic or problem
• Collected through direct observation or study

Balancing qualitative and quantitative UX research

Employ qualitative research to explore ideas and discover new insights, and then tap into quantitative research methods to test a hypothesis or final solution.

While qualitative and quantitative research yields different data types, they are both essential for conducting effective research and getting actionable insights. Not one method can give you a complete picture, so using both in combination is often the best way to ensure you’re making the right product decisions that fit with your business goals.

Qualitative and quantitative research reinforce each other and help to triangulate the research results. You can be surer of the validity of your findings if both qualitative and quantitative approaches produce convergent results.

Usually, the best solution is built using a combination of insight sources. For example, you can kick-off the discovery phase of a project with qualitative research, and run user interviews to understand people’s needs, preferences, and opinions.

After this initial batch of research studies, the product and design team can start building an incipient solution, usually in the form of a low-fidelity prototype or mockups. The initial solution is then tested through interviews and surveys, and the feedback gathered can help you iterate on the solution until final.

Sometimes you want to start with a round of qualitative methods such as interviews, fly-on-the-wall observations, and diary studies to explore the field and follow up with a quantitative study on a larger sample to generalize the results.

Lastly, when you’ve arrived at a final product, doing user testing quantitatively will help you ensure your solution is easy to use, usable, and intuitive for the end-users—and there are no significant issues with the design before going into the development phase. This mix and match of methods is the best way to research and test during the entire design process until arriving at a solution.

Very often, the solution is built on mixed methods–less quantitative versus qualitative–and more somewhere in-between the two.

In the next chapter, we will dive deeper into common types of research you can use such as tree testing, card sorting, and usability studies, and help you choose the right one for you.

Frequently asked questions

What is quantitative UX research?

Quantitative research is a research methodology used to collect and analyze numerical data, identify patterns, make predictions, and generalize findings about a target audience.

What is qualitative UX research?

Qualitative UX research is a research methodology used to answer questions and understand the motivations, thoughts, and attitudes of a target audience.

What are examples of quantitative research methods?

Quantitative user research methods include usability testing, web analytics (or app analytics), card sorting, and surveys.

What are examples of qualitative research methods?

Qualitative user research methods include user interviews, diary studies, focus groups, card sorting, and usability testing.

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Quantitavive UX Research vs. Qualitative — a Comprehensive Guide (2023)

In the ever-evolving realm of user experience (UX) design, research acts as the compass that guides designers towards creating delightful and intuitive digital experiences.

As UX designers, we understand the vital role research plays in uncovering user insights, informing design decisions, and ultimately delivering exceptional products. However, within the expansive field of UX research, two dominant methodologies reign supreme: qualitative and quantitative research.

Qualitative and quantitative approaches each offer distinct lenses through which we can view user behavior, preferences, and needs. Yet, the question often arises: which research methodology should UX designers embrace to extract meaningful insights and optimize their design process?

In this article, we embark on a journey to demystify the complexities of qualitative and quantitative UX research specifically tailored to the discerning minds of UX designers.

What Is Quantitative UX Research

Quantitative UX research is a systematic approach to gathering and analyzing numerical data to gain insights into user behavior and preferences. It involves collecting data on a large scale, often through surveys, experiments, and analytics, with the goal of obtaining statistically significant results.

In quantitative UX research, designers use metrics, measurements, and statistical analysis to quantify user behaviors, attitudes, and opinions. The focus is on generating objective and measurable data that can be analyzed to identify patterns, trends, and correlations.

This data-driven approach provides designers with quantitative evidence to support decision-making throughout the design process.

Quantitative research in UX provides designers with valuable insights into user behavior at scale, helping them make data-informed decisions, identify usability issues, validate design hypotheses, and track the impact of design changes over time.

It complements qualitative research by providing a broader understanding of user preferences and behaviors, allowing designers to make informed decisions based on statistically significant data. Let’s review the various quantitative ux research methods.

Quantitative UX Research Methods

There are several quantitative UX research methods that designers can employ to gather data and insights. Here are some commonly used quantitative methods in the field of UX:

• Surveys: Surveys are one of the several quantitative research methods. It involves collecting data from a large number of participants using structured questionnaires. They can be administered online or in person and are useful for gathering information on user preferences, satisfaction, demographics, and more.
• A/B Testing: A/B testing -one of the most common quantitative user research methods- compares two or more variations of a design element or feature to determine which performs better based on predefined metrics. It allows designers to test hypotheses, evaluate design choices, and optimize user experiences.
• Analytics and User Tracking: Utilizing web analytics tools or tracking software, designers can gather quantitative data on user behavior within a digital product. Metrics such as click-through rates, page views, time spent on pages, or conversion rates provide insights into user engagement and interactions.
• Behavioral Analysis: Behavioral analysis involves studying large-scale user behavior data to identify patterns and trends. This can include analyzing user flows, funnels, drop-off points, or frequency of interactions to gain insights into user journeys and optimize the user experience.
• Task Performance Metrics: Task performance metrics measure specific aspects of user performance, such as task completion time, error rates, or efficiency. These metrics provide quantitative data on the usability and effectiveness of a design and can help identify areas for improvement.
• Eye Tracking: Eye tracking technology is used to measure and analyze where users look on a screen or interface. It provides quantitative data on visual attention, gaze patterns, and heat maps, which can inform design decisions related to visual hierarchy, information placement, and visual cues.
• Clickstream Analysis: Clickstream analysis involves analyzing the sequence of user actions and interactions within a digital product. It helps identify navigation patterns, user flows, and areas of interest or concern.
• Quantitative Interviews: In quantitative interviews, researchers use a structured interview format to ask predefined questions to participants. The responses are quantified and analyzed for statistical trends and patterns.

These are just a few examples of quantitative UX research methods. Each method has its strengths and limitations, and the choice of methods depends on the research objectives, the target audience, and the available resources.

Often, a combination of qualitative and quantitative research methods can provide a more comprehensive understanding of the user experience.

Expert Considerations to Effectively Do Quantitative UX Research

Quantitative UX reasearch and successfuly interpreting quantitative metrics requires certain aspects that every UX researcher must keep in mind.

1. Plan for high-quality and relevant quantitative UX data

Proper interpretation of quantitative UX metrics starts before gathering any data. There are overarching questions that practitioners need to ask to keep on track and make sound interpretations. 

Some questions to consider are: What are the goals and objectives of the quantitative research you are gathering? What research questions are attempting to be answered with quantitative UX metrics? What methods will be used to interpret data? Who are the stakeholders who will use the data? 

Investing the time to define and answer these questions allow UX researchers to focus on highly relevant metrics to goals and objectives. 

2. Focus on UX-related metrics and not business metrics

There can be an overwhelming amount of metrics for business analytics. So the first step is to narrow it down so that time isn’t wasted focusing on irrelevant data to UX. 

Pro tips: understand UX Metrics versus KPIs. 

UX Metrics are quantitative data used to measure, compare, and track users’ experience interacting with a digital product over time. These are associated with user behaviors and attitudes. KPIs (key performance indicators) are quantitative data used to measure, compare, and track the overall goals. These goals typically are tied to revenue, growth, retention, and user counts. 

It is essential to focus on UX data that aligns with your goals and objectives for research.

3. Have a streamlined data wrangling process in place

A critical part of the quantitative data interpretative process is ensuring data is reliable before analyzing and leveraging it for insights. At this junction is where data wrangling (the process of discovering, structuring, cleaning, enriching, validating, and publishing the data) comes in. This process can be very lengthy and time-consuming. 

Data professionals spend as much as 80% of their time preparing data for analysi s. UX professionals cannot afford this much of their time to be sucked up in cleaning and organizing data. But suppose your research operations have streamlined processes for how to wrangle data. In that case, this saves a lot of time and removes the risk of gleaning insights and making interpretations from incomplete, unreliable, or inconsistent data.

4. Use storytelling to communicate findings

Data visualization is an art. And explaining data visuals is a craft. Not many can do these two things well. This is why storytelling is such a powerful skill. Graphs and charts are great, but if a researcher cannot tell a story to explain the data, the findings have minimal impact on business decisions. Additionally, people, including business leaders, are moved by stories.

It is essential to know how to choose the right data visualization type. Generally, there are four goals for data visualization types: 1. showing relationships, 2. showing distribution, 3. showing the composition, or 4. making comparisons. 

Asking the following questions will help you define the best visualization type for the right audience: 

• What is the story you want to tell?
• Who is the audience you want to tell the story to?
• Do we want to analyze trends?
• Do we want to demonstrate composition?
• Do we want to compare two or more sets of values?
• Do we want to show changes over time?
• How will we show UX Metrics?

Once these questions are answered, it becomes easier to decide if a pie chart, a line chart, a spider chart, a bar chart, or a scatter plot is the best visualization type to tell the user experience story.

5. Synthesize your insights and draw valuable conclusions

Now comes the moment where the synthesis of quantitative UX metrics data serves as a change agent for the user experience. Extract facts from the data. Remain objective by being aware of the pitfalls previously discussed. And make interpretations of the data. The goal is to generate valuable recommendations. 

Good recommendations are:

• Constructive. They offer a solution rather than focusing on the problem revealed by the data.
• Specific. They identify wherein the user experience recommendations are most applicable.
• Actionable. Suggestions should be active. Use language that is active rather than passive to inspire change. 
• Concise. Plenty of recommendations can be generated from any given set of UX data, but not all of them will significantly impact the user experience. Prioritize the most important ones. 
• Measurable. Good recommendations can be measured so that there can be evidence a change has occurred and an impact has been made.
• Balanced. Identify both the strengths and weaknesses.

What is Qualitative UX Research

Qualitative UX research is an investigative approach that focuses on gathering rich, descriptive insights and understanding the subjective experiences, attitudes, and motivations of users.

Unlike quantitative research, qualitative research aims to uncover the “why” behind user behavior rather than focusing solely on numerical data.

Qualitative UX research methods involve observing and engaging with users in a more open-ended and exploratory manner, allowing for in-depth exploration of user perspectives.

This type of research provides designers with a deep understanding of user needs, pain points, and aspirations, which can inform design decisions and drive empathy-driven solutions.

Qualitative research allows designers to gain a deeper understanding of user needs, motivations, and emotions. It helps uncover nuances, user pain points, and opportunities for improvement that quantitative data alone may not reveal.

By leveraging qualitative insights, designers can generate empathy, enhance user engagement, and create user-centered experiences that address real user challenges.

It’s worth noting that qualitative and quantitative research are often used together in a complementary manner, with qualitative research providing a foundation for hypothesis generation and quantitative research validating and measuring the impact of design decisions.

Qualitative research methods in UX

Qualitative research methods focus on gathering rich, in-depth insights into user experiences, attitudes, and motivations.

These qualitative user research methods allow designers to understand the “why” behind user behavior and provide valuable context for design decisions. Here are some commonly used qualitative research methods in UX:

• User Interviews: These qualitative methods require one-on-one or group interviews with participants to gather detailed information about their experiences, behaviors, needs, and goals. These interviews can be structured or semi-structured, allowing for open-ended discussions.
• Contextual Inquiry: Observe users in their natural environment while they engage with a product or service. This method provides insights into how users interact with a design in real-life situations, uncovers pain points, and identifies opportunities for improvement.
• Diary Studies: Ask participants to keep a diary or journal to record their experiences, thoughts, and behaviors over a specific period. Diary studies provide longitudinal insights into users’ lives, allowing designers to understand their daily routines, challenges, and emotional responses.
• Usability Testing with Think Aloud: A solid approach is to observe users as they perform tasks while verbalizing their thoughts and impressions. This method provides real-time insights into users’ decision-making processes, frustrations, and successes during the interaction with a design.
• Focus Groups: Facilitate group discussions with participants to explore shared experiences, opinions, and perceptions. Focus groups encourage participants to build upon each other’s ideas, generate insights, and identify common themes or patterns.
• Card Sorting: Engage users in organizing and categorizing information by asking them to sort and group items into meaningful categories. This method helps designers understand users’ mental models and how they perceive and organize information.
• Cognitive Walkthroughs: Walk through a design or prototype with participants while they share their thoughts and decision-making process. Cognitive walkthroughs help identify potential usability issues and gaps in user understanding.
• Ethnographic Research: Conduct in-depth, immersive studies in users’ natural environments over an extended period. Ethnographic research allows designers to deeply understand users’ cultural context, behaviors, and needs.
• Emotional Mapping: Use techniques such as user diaries, interviews, or visual exercises to explore users’ emotional responses and associations with a product or service. Emotional mapping helps designers create emotionally resonant experiences.
• Prototype Testing and Iteration: One of the several qualitative methods is tp share low-fidelity or high-fidelity prototypes with users and gather their feedback through observations, interviews, or usability testing. Prototyping allows designers to validate ideas, refine designs, and iterate based on user insights.

These qualitative research methods provide rich data and insights that go beyond numbers and metrics, helping designers gain a deep understanding of users’ experiences, perceptions, and needs. Combining different methods can offer a comprehensive view of user perspectives and inform user-centered design decisions.

When conducting quantitative UX research, there are several expert considerations to keep in mind to ensure the effectiveness of your study. Here are some key considerations.

1. Clearly define research objectives

Begin by defining clear and specific research objectives. Clearly articulate what you aim to achieve through your quantitative research and what specific questions you want to answer. This will guide your study design and data analysis.

2. Use validated measurement instruments

When selecting or creating measurement instruments such as surveys or questionnaires, use established and validated tools whenever possible. Validated instruments have been rigorously tested for reliability and validity, ensuring the accuracy and consistency of the data collected.

3. Pay attention to sampling and avoid bias in data collection

Ensure that your sample is representative of your target population or user group. Consider factors such as demographics, user characteristics, or usage patterns when selecting participants. A well-designed sampling strategy is crucial for the generalizability and validity of your findings.

Also, take steps to minimize bias in data collection. Provide clear instructions to participants, use neutral language, and avoid leading questions that may influence their responses. Additionally, consider factors such as the order of questions or the presentation of stimuli to mitigate potential biases.

4. Collect sufficient data

Ensure that your sample size is adequate to achieve statistical significance. Power analysis can help determine the appropriate sample size based on the effect size you expect to detect, the desired level of confidence, and statistical power.

5. Use appropriate statistical analysis and consider mixed methods

Choose appropriate statistical methods to analyze your quantitative data. Descriptive statistics, inferential statistics (e.g., t-tests, ANOVA, regression), and correlation analysis are common techniques used in quantitative UX research. Consult with a statistician if needed to ensure the accuracy and validity of your analysis.

Also, consider combining quantitative data with qualitative insights to gain a more comprehensive understanding. Integrating qualitative data can provide valuable context and shed light on the “why” behind quantitative findings, enriching the interpretation of your results.

6. Interpret results within context and communicate findings effectively

Interpret your quantitative findings in the context of your research objectives, user behavior, and broader UX considerations. Avoid drawing overly simplistic or misleading conclusions and consider alternative explanations or factors that may influence the results.

Also, present your quantitative findings in a clear and concise manner, using visualizations and data summaries that are easily understandable to both technical and non-technical stakeholders. Clearly communicate limitations and uncertainties associated with the research findings.

7. Iterate and refine

Remember that quantitative UX research is an iterative process. Continuously refine your research methods based on feedback, learnings, and new insights gained. Use findings to inform design iterations and further research efforts.

For UX practitioners, the volume of quantitative data available in today’s digital world is vast. And correctly interpreting quantitative UX metrics can be a daunting task. While it’s worth investing in highly technical skills, often, it’s more about processes that enable sound interpretations of UX metrics. The key is to remain objective, focus on relevant data, have simplified procedures for data cleaning and analysis, tell a good story with said data, and draw valuable conclusions to improve the user experience. Interpreting quantitative UX metrics is more about the process than sophistication in statistical knowledge (some tools take care of this). The goal is to have simplified, focused, and repeatable processes.

Interested in UX Testing?

Data visualizations, about the author: huyen hoang.

Huyen Hoang is a User Experience Researcher at Codelitt . Codelitt helps companies create better product experiences for their users by designing and building people-driven solutions with the speed, technology, and innovation of a startup.

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A complete guide to qualitative vs quantitative ux research methods.

How can we build this product?

What can we do to improve this product?

Why are the conversion rates so low?

These are some of the burning questions that every organization faces. In today’s competitive world, understanding consumer behaviour is the quintessential ingredient for the recipe of success.

After all, users are at the heart of any experience of a product, and up to 90% of users are reported to leave using an application due to bad performance. Billions of dollars can be lost when businesses do not prioritize UX Research and turn to the guesswork game.

What is UX Research?

Also known as user experience research, it involves the study of user needs and preferences. While developing any product, platform, or service, it is imperative to understand the user's behaviour. With the help of UX Research, businesses can understand the different aspects of product development and customize it in a way that aligns with the user's needs. This process also helps in improving the product.

With the growing complexities of consumer demand and the rising need for customized products, understanding these changing trends is the need of the hour. One of the ways is to research and analyse consumer behaviour that can help in boosting experience, thus driving conversion.

Studies have proved that a good user experience can be rewarding to companies . Hence, companies are now actively investing in harnessing the power of UX Research. Moreover, with this surge of new technologies like ML and AI, it has become easier to understand the nuances of consumer preferences, thus ensuring better products and services.

  UX research has two subsets:

• Qualitative Research
• Quantitative Research

Qualitative and quantitative research methods are both crucial in understanding user experience (UX) that translates into informed design decisions. Each method offers unique insights and benefits, and often, they are used together to provide a comprehensive understanding of user needs and behaviours. 

This blog takes you through both these concepts and will unfold its key features and applications.

What is Quantitative Research in UX?

‍ Source: Image of Quantitative Research in UX

As the name highlights, this method of UX research focuses on tangible parameters to understand human behavior and assess the usability of a product. The focus of quantitative research in UX is to collect quantifiable data from a large sample and use it to analyze the trends in the market.

To measure user behaviour, preferences, choices, and experiences by analyzing numerical data.

It gives answers to questions such as "what" and "how much." For example:

What percentage of users were able to complete the task in under 2 minutes?

How many errors did the users face while completing this task?

Key Features of Quantitative Research in UX

Data collection.

Collecting data is a critical aspect of quantitative research. Hence, the researchers deploy different methods like surveys, A/B testing, and others to collect the data. This data is usually in the form of clicks or time spent on a webpage or product or satisfaction rates.

Focus on Measurable Data

The focus of this method is on collecting quantifiable data from a large set, such that this information can be analyzed and help in deriving accurate results.

Since quantitative research depends on measurable data, the results are more authentic and give substantial support for a change or improvement.

Large sample sizes

The method focuses on a larger sample size, which ensures that the results are statistically significant. Hence, we can generalize the results to the whole population.

‍Limitations of Quantitative Research in UX

Although quantitative research in UX can help in giving measurable outcomes, it cannot provide support for why the consumer is behaving in a particular way. To get a more sustainable proof, it is important to follow a combined strategy of using qualitative research methods.

It lacks the depth and context that the insights generated from the qualitative UX research bring to the table.Overall, quantitative research is a powerful tool for UX professionals.

By using data to measure user behaviour, you can create products that are not only useful but also enjoyable to use.

Some of the Common Quantitative UX Research Methods

Here's a breakdown of some popular quantitative UX research methods, including their descriptions, features, and examples:

This helps in gathering feedback through structured questions to quantify the preferences, opinions, or experiences of users with a product. However, it is important to mention that this method can also be used in qualitative testing based on the kind of questions it asks. For example, close-ended questions can be a part of the quantitative research method. Conversely, open-ended surveys allow users to provide detailed, narrative responses to gather their feedback and understand their sentiments, thus falling in the category of qualitative research.

• It focuses on a large audience
• The data points are measurable and hence is easy to analyze
• It helps in measuring user satisfaction with a new product, service, or website design
• Understand the interests of the consumer
• Tracking the consumer behaviour pertaining to a particular design

2. A/B testing

This compares two or more design variations to determine which performs better (leading to higher user engagement or conversion rates).

• It highlights the impact of specific design changes.
• Optimize user experience based on real user behaviour.
• Quantify the improvement in conversion rates or other metrics.
• Test two different headlines on a landing page to see which one drives more clicks.
• Compare two checkout form designs to see which one reduces cart abandonment rates.
• Experiment with different button colours to see which one gets more users to sign up.

3. Website Analytics

Understanding website analytics can unfold several patterns, like user behaviour on the website, engagement rate, clicks, and conversion rates. All these insights can help highlight trends and patterns.

• Get a detailed insight into the website traffic.
• Identify user engagement
• Highlights the popular trend on the website
• Assess the effectiveness of marketing campaigns.
• Find out the bounce rate of different web pages
• Highlight the popular content amongst the user

These are some of the common quantitative research methods. Using them, the researcher can gain valuable insights into consumer behavior that can help in improving the overall experience.‍

What is Qualitative Research in UX?

Source: Image of Qualitative Research in UX

Unlike quantitative research, qualitative research in UX design focuses on understanding the reason behind the consumer's behaviour or preference.

It focuses on collecting descriptive and non-numerical data points such as the motivation behind the purchase decision, the need, and the thought process of the consumers. Analogous to quantitative research, the qualitative methods force the subjective experience of the user.

To find out the underlying motivations, emotions, and perceptions of users.

It gives answers to questions such as "why" and "how." For example:

What challenges did the user face while signing up?

(This answers the question: Why the user took a longer time to sign up/ couldn't sign up)

What improvements could improve your experience of the app?

(This answers the question: How can we improve the user experience of the app)

Key Features of Qualitative Research in UX:

Focus on the "why".

As mentioned above, quantitative research deals with numbers, but qualitative research focuses on addressing the why behind the behavior of consumers. It helps the researcher understand the thought processes and emotions that drive the decisions of the consumers.

Uncover User Needs and Experiences

The qualitative research method helps to understand the needs of the consumers. It also sheds light on the challenges and overall experience of the consumers, which eventually helps build a product or service that is in coherence with the demands of the consumers. It relies on various methods like usability testing and interviews to gather this information.

In-depth Exploration

Understanding the nuances of consumer behavior is the foundation of qualitative research in UX. Focusing on smaller and more targeted users helps the researcher unfold the depth of a specific topic and gain detailed insight.

Flexibility

While using the qualitative research methods in UX, the researchers have flexibility in their approach.

Sample Size

The sample size is much smaller, typically till the saturation point, where the responses from the participants get repetitive. According to the NN Group, the recommended sample size so far has been between 5 to 50. The relatively smaller sample size allows for an in-depth exploration of user experiences.

Data Analysis

Thematic analysis, content analysis, or qualitative coding are used to identify the patterns, themes, and insights from the data collected.

Some of the Common Qualitative UX Research Method

Qualitative UX research methods help you understand the "why" behind user behavior. Here's a breakdown of some popular methods, including their descriptions, features, and examples:

1. User Interviews

This involves one-on-one conversations with users to understand their preferences, motivations, and experiences.

• Get in-depth insight into the user needs
• Highlight the reason behind user behavior
• Gain rich, detailed insights into user needs and motivations.
• Uncover unspoken thoughts and feelings that might not appear in surveys.
• Tailor questions to specific user segments.
• Interviewing e-commerce shoppers to understand their purchase decision process.
• Talking to new mobile app users to identify onboarding pain points.
• Discussing users' mental models for a complex software program.

2. Usability Testing

This involves observing the users as they interact with the product to find usability issues and gather feedback.

• Get first-hand information on how the user interacts with the designs
• Find out if the user is facing any confusion while using the website
• Testing the checkout flow of potential customers
• Watch how the user is completing a particular task within the app

3. Focus Group

This brings together a small group of participants to discuss their attitudes and perceptions regarding a product.

• Gain insights from group dynamics and user interactions.
• Identify common themes and concerns among a specific user segment.
• Spark new ideas and areas for exploration based on user feedback.
• Conducting a focus group with new parents to understand their needs for a baby monitoring app.
• Gathering feedback from experienced gamers on a new game concept.
• Discussing user expectations and pain points related to a new financial service platform.

By using these qualitative methods, UX researchers can build a comprehensive understanding of user needs, motivations, and behaviors.

Limitation of Qualitative UX Research Method

Such insights cannot be statistically validated and generalized to the larger population.

Summary of Qualitative Research vs Quantitative Research Methods

Qualitative UX Research

Quantitative UX Research

Type of Data

Focuses on non-numerical data.

Measurable and tangible data.

In-depth understanding

More generalized approach

Method of Data Collection

Interview, Focus Group, Usability Testing.

Surveys, A/B Testing, Website Analytics

Gives the reason behind the consumer behaviour and also highlights the user perception and motivation behind the decision.

Gives statistical validity and tangible reasons to support the decision and highlights the trend and patterns.

Limitations

It gives a more subjective outcome, and the process is time-consuming.

This method lacks depth and is not able to highlight the nuances of consumer behaviour. Also, there is a possibility of data biases.

Final Verdict: Qualitative vs Quantitative Research, Which is Better?

Both quantitative and qualitative research in UX have their pros and cons, offering the researcher different options. The right approach would be following mixed-methods research.

It is necessary for companies to recognize which methodology will be the most beneficial to their product success by asking the right questions. When successful UX research is conducted, it can lead to products and experiences that resonate with the diverse needs and stories of your users and user journey.

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The goal of quantitative research in UX is to measure user behavior, preferences, choices, and experiences by analyzing numerical data.

Quantitative research provides answers to questions such as "what" and "how much." For example, it can determine the percentage of users able to complete a task in under a certain time frame or the number of errors users face during a task.

Common methods include surveys, A/B testing, and behavioral data analysis. Surveys gather feedback through structured questions, A/B testing compares design variations, and behavioral data analysis examines user actions within a digital product.

The goal of qualitative research in UX is to uncover the underlying motivations, emotions, and perceptions of users.

Qualitative research typically involves a smaller sample size compared to quantitative research, allowing for in-depth exploration. Data analysis in qualitative research focuses on identifying patterns, themes, and insights through methods such as thematic analysis or content analysis.

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When to Use Which User-Experience Research Methods

July 17, 2022 2022-07-17

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The field of user experience has a wide range of  research methods  available, ranging from tried-and-true methods such as lab-based usability testing to those that have been more recently developed, such as unmoderated UX assessments.

While it's not realistic to use the full set of methods on a given project, nearly all projects would benefit from multiple research methods and from combining insights. Unfortunately, many design teams only use one or two methods that they are most familiar with. The key question is what to use when.

In This Article:

Three-dimensional framework, the attitudinal vs. behavioral dimension, the qualitative vs. quantitative dimension, the context of product use, phases of product development (the time dimension), art or science, 20 ux methods in brief.

To better understand when to use which method, it is helpful to view them along a  3-dimensional framework  with the following axes:

• Attitudinal vs. Behavioral
• Qualitative vs. Quantitative
• Context of Use

The following chart illustrates where 20 popular methods appear along these dimensions:

This distinction can be summed up by  contrasting "what people say" versus "what people do"  (very often the two are quite different). The purpose of attitudinal research is usually to understand or measure people's stated beliefs, but it is limited by what people are aware of and willing to report.

While most  usability studies should rely on behavior , methods that use self-reported information can still be quite useful to designers. For example,  card sorting  provides insights about users' mental model of an information space and can help determine the best information architecture for your product, application, or website.  Surveys  measure and categorize attitudes or collect self-reported data that can help track or discover important issues to address.  Focus groups tend to be less useful for usability  purposes, for a variety of reasons, but can provide a top-of-mind view of what people think about a brand or product concept in a group setting.

On the other end of this dimension, methods that focus mostly on behavior seek to understand "what people do" with the product or service in question. For example  A/B testing  presents changes to a site's design to random samples of site visitors but attempts to hold all else constant, in order to see the effect of different site-design choices on behavior, while  eyetracking  seeks to understand how users visually interact with a design or visual stimulus.

Between these two extremes lie the two most popular methods we use: usability studies and  field studies . They utilize a mixture of self-reported and behavioral data and can move toward either end of this dimension, though leaning toward the behavioral side is generally recommended.

The distinction here is an important one and goes well beyond the narrow view of qualitative as in an open-ended survey question. Rather, studies that are qualitative in nature generate data about behaviors or attitudes based on observing or hearing them  directly , whereas in  quantitative studies , the data about the behavior or attitudes in question are gathered  indirectly , through a measurement or an instrument such as a survey or an  analytics tool . In field studies and usability testing, for example, researchers directly observe how people use (or do not use) technology to meet their needs or to complete tasks. These observations give them the ability to ask questions, probe on behavior, or possibly even adjust the study protocol to better meet study objectives. Analysis of the data is usually not mathematical.

In contrast, the kind of data collected in quantitative methods is predetermined — it could include task time, success, whether the user has clicked on a given UI element or whether they selected a certain answer to a multiple-choice question. The insights in quantitative methods are typically derived from mathematical analysis, since the instrument of data collection (e.g., survey tool or analytics tool) captures such large amounts of data that are automatically coded numerically.

Due to the  nature of their differences ,  qualitative  methods are much better suited for answering questions about  why  or  how to fix  a problem, whereas  quantitative  methods do a much better job answering  how many  and  how much  types of questions. Having such numbers helps prioritize resources, for example to focus on issues with the biggest impact. The following chart illustrates how the first two dimensions affect the types of questions that can be asked:

The third distinction has to do with how and whether participants in the study are using the product or service in question. This can be described as:

• Natural  or near-natural use of the product
• Scripted  use of the product
• Limited  in which a limited form of the product is used to study a specific aspect of the user experience
• Not using  the product during the study (decontextualized)

When studying  natural use  of the product, the goal is to minimize interference from the study in order to understand behavior or attitudes as close to reality as possible. This provides greater external validity but less control over what topics you learn about. Many ethnographic field studies attempt to do this, though there are always some observation biases. Intercept surveys and data mining or other analytic techniques are quantitative examples of this.

A  scripted  study of product usage is done in order to focus the insights on specific product areas, such as a newly redesigned flow. The degree of scripting can vary quite a bit, depending on the study goals. For example, a benchmarking study is usually very tightly scripted, so that it can produce reliable  usability metrics by ensuring consistency across participants.

Limited  methods use a limited form of a product to study a specific or abstracted aspect of the experience. For example, participatory-design methods allow users to interact with and rearrange design elements that  could  be part of a product experience, in order discuss how their proposed solutions would meet their needs and why they made certain choices. Concept-testing methods employ an expression of the idea of a product or service that gets at the heart of what it would provide (and not at the details of the experience) in order to understand if users would want or need such a product or service.  Card sorting and tree testing focus on how the information architecture is or could be arranged to best make sense to participants and make navigation easier.

Studies where the  product is not used  are conducted to examine issues that are broader than usage and usability, such as a study of the brand or discovering the aesthetic attributes that participants associate with a specific design style.

Many of the methods in the chart can move along one or more dimensions, and some do so even in the same study, usually to satisfy multiple goals. For example, field studies can focus a little more on what people say (ethnographic interviews) or emphasize studying what they do (extended observations); concept testing, desirability studies, and card sorting have both qualitative and quantitative versions; and eyetracking can be natural or scripted.

Another important distinction to consider when making a choice among research methodologies is the phase of product development and its associated objectives.  For example, in the beginning of the product-development process, you are typically more interested in the strategic question of what direction to take the product, so methods at this stage are often generative in nature, because they help generate ideas and answers about which way to go.  Once a direction is selected, the design phase begins, so methods in this stage are well-described as formative, because they inform how you can improve the design.  After a product has been developed enough to measure it, it can be assessed against earlier versions of itself or competitors, and methods that do this are called summative. This following table describes where many methods map to these stages in time:

While many user-experience research methods have their roots in scientific practice, their aims are not purely scientific and still need to be adjusted to meet stakeholder needs. This is why the characterizations of the methods here are meant as general guidelines, rather than rigid classifications.

In the end, the success of your work will be determined by how much of an impact it has on improving the user experience of the website or product in question. These classifications are meant to help you make the best choice at the right time.

Here’s a short description of the user research methods shown in the above chart:

Usability testing (aka usability-lab studies): Participants are brought into a lab, one-on-one with a researcher, and given a set of  scenarios that lead to tasks  and usage of specific interest within a product or service.

Field studies : Researchers  study participants in their own environment (work or home), where they would most likely encounter the product or service being used in the most realistic or natural environment.

Contextual inquiry : Researchers and participants collaborate together in the participants own environment to inquire about and observe the nature of the tasks and work at hand. This method is very similar to a field study and was developed to study complex systems and in-depth processes.

Participatory design : Participants are given design elements or creative materials in order to construct their ideal experience in a concrete way that expresses what matters to them most and why.

Focus groups : Groups of 3–12 participants are led through a discussion about a set of topics, giving verbal and written feedback through discussion and exercises.

Interviews : a researcher meets with participants one-on-one to discuss in depth what the participant thinks about the topic in question.

Eyetracking : an eyetracking device is configured to precisely measure where participants look as they perform tasks or interact naturally with websites, applications, physical products, or environments.

Usability benchmarking : tightly scripted usability studies are performed with larger numbers of participants, using precise and predetermined measures of performance, usually with the goal of tracking usability improvements of a product over time or comparing with competitors.

Remote moderated testing :  Usability studies are conducted remotely , with the use of tools such as video conferencing, screen-sharing software, and remote-control capabilities.

Unmoderated testing: An automated method that can be used in both quantitative and qualitative studies and that uses a specialized research tool to capture participant behaviors and attitudes, usually by giving participants goals or scenarios to accomplish with a site, app, or prototype. The tool can  record a video stream of each user session, and can gather usability metrics such as success rate, task time, and perceived ease of use.

Concept testing : A researcher shares an approximation of a product or service that captures the key essence (the value proposition) of a new concept or product in order to determine if it meets the needs of the target audience. It can be done one-on-one or with larger numbers of participants, and either in person or online.

Diary studies : Participants are using a mechanism (e.g., paper or digital diary, camera, smartphone app) to record and describe aspects of their lives that are relevant to a product or service or simply core to the target audience.  Diary studies  are typically longitudinal and can be done only for data that is easily recorded by participants.

Customer feedback : Open-ended and/or close-ended information is provided by a self-selected sample of users, often through a feedback link, button, form, or email.

Desirability studies : Participants are offered different visual-design alternatives and are expected to associate each alternative with a set of attributes selected from a closed list. These studies can be both qualitative and quantitative.

Card sorting : A quantitative or qualitative method that asks users to organize items into groups and assign categories to each group. This method helps  create or refine the information architecture  of a site by exposing users’  mental models .

Tree testing : A quantitative method of testing an information architecture to determine how easy it is to find items in the hierarchy. This method can be conducted on an existing information architecture to benchmark it and then again, after the information architecture is improved with card sorting, to demonstrate improvement.

Analytics : Analyzing data collected from user behavior like clicks, form filling, and other recorded interactions. It requires the site or application to be instrumented properly in advance.

Clickstream analytics:  A particular type of analytics that involves analyzing the sequence of pages that users visit as they use a site or software application.

A/B testing  (aka  multivariate testing , live testing, or bucket testing): A method of scientifically testing different designs on a site by randomly assigning groups of users to interact with each of the different designs and measuring the effect of these assignments on user behavior.

Surveys : A quantitative measure of attitudes through a series of questions, typically more closed-ended than open-ended .  A survey that is triggered during the use of a site or application is an intercept survey, often triggered by user behavior. More typically, participants are recruited from an email message or reached through some other channel such as social media.

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