Qualitative and quantitative research are easy to get mixed up. Both are widely used across fields as diverse as software development, healthcare, and real estate. They're long words that look almost identical on the page. They're often discussed in proximity to each other, too, as differing but complementary paths toward answering complex questions about people’s behaviors, feelings, and preferences. 

Because of this, and because qualitative and quantitative research (along with quantitative usability testing research) are both such important concepts in the world of user research, it can be helpful to brush up on their bedrock definitions and applications. Knowing which research method to use when, and how to combine them effectively, is a key way to level up your research skills. 

Whether you’re new to doing research like this, find yourself being asked to do more of it in your job, or just want a refresher, this article can help. 

What's the difference between qualitative vs quantitative research? 

To put it very simply, quantitative research answers questions like “how many?” or “how much?” while qualitative research explores questions like “why?” or “how?” 

You can look to their names for some clues. Quantitative research measures quantities – like “how many people used our app in the first month?” or “how much did sales increase as a result of our recent social campaign?” – while qualitative research explores qualities – like “how do people feel about our rebrand?” or “why do our superusers love our software?”. 

Another easy differentiating factor can be the tools being used. If you’re using analytics software or crunching numbers from large-scale surveys, you’re probably utilizing quantitative research methods. On the other hand, if you’re parsing in-depth interviews with one or more users, or surveying videos of usability tests, you’re likely in the realm of qualitative research. Numbers are quantitative; quotes are qualitative. 

It’s also worth clarifying that, while these research methodologies differ, they aren’t separate skillsets performed by different types of researchers. Rather, qualitative and quantitative research techniques are used together across teams like customer experience research, product, strategy and insights, service design, accessibility, and market research. Just as a carpenter uses both a hammer and a saw, so too do these professionals use both qualitative and quantitative research. 

Let’s dive into both in a little more depth so you can better understand when and how to use them.

What is quantitative research?

Quantitative research is numerical and statistical, making it ideal for objective assessments. If you’re evaluating app analytics like monthly active users or marketing metrics like click-through rates, you’re dealing with quantitative data. It’s often generated with structured tools, like surveys with multiple-choice options, because they can be more clearly counted and quantified as percentages. 

Quantitative research aims to be generalizable: its results can be extrapolated to the broader population. For example, consider A/B testing, which is the practice of running two variants on a test audience before rolling one version out to a broader audience. If you were A/B testing two menu configurations with a small sample audience, and one provided much stronger engagement in a target metric, you may then roll the “winning” variant out to all users. In doing so, you would be relying upon the principle of generalizability: the understanding that the sample audience was representative of all users. 

Types of quantitative research

While there are many types of quantitative research, here are some varieties to keep in mind.

Surveys

Surveys are structured questionnaires that collect data from a sample of participants, which can then be extrapolated to a broader audience. Common formats include Likert scales (“on a scale of 1-5, how much do you …”), multiple choice options (“which feature would you describe as your favorite …”), and yes-or-no style questions (“would you recommend our product to a colleague?”). 

Experimental research

Experiments control conditions to study cause-and-effect relationships. Think of the scientific method here, in which a hypothesis is carefully tested by manipulating one variable to observe its effect. The A/B test outlined above is a good example of experimental research.

Longitudinal research

Longitudinal research collects data from the same group of people over an extended period. If you wanted to explore an app’s usage lifecycle across different types of users, you may use longitudinal research to discover that, for example, users who buy annual memberships tend to engage in social features more.

Descriptive research

Descriptive research describes a phenomenon as it exists already, without researcher input. An example of descriptive research would be evaluating user journeys via web analytics software. 

Pros and cons of quantitative research

Neither qualitative nor quantitative research is inherently better than the other. Each has its own set of pros and cons. Here are some of the advantages of quantitative research: 

• Objectivity: Structured data collection minimizes the possibility of bias in interpretation. Numbers are hard to argue with. 

• Generalizability: As mentioned above, good quantitative research is structured such that findings within a sample can be applied to a larger group. 

• Replicability: Theoretically, if you ran the same quantitative research project a second time, you’d get similar results. An A/B test run a second time should produce the same winner, for example.

• Quantifiability: Quantitative research should yield clear metrics and measurable results.

Some of the disadvantages of quantitative research include: 

• Lack of depth: Hard numbers are great, but they can’t explain the underlying motivations or feelings driving them. 

• Rigidity: There’s little room for improvisation in the research process, given its focus on structure and replicability. 

• Potential for misinterpretation: As useful as hard numbers can be, they also lack texture and can lead to incorrect conclusions. For example, declining engagement numbers could lead some to believe a recent redesign is to blame, but it may simply be due to seasonality in usage. 

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Examples of quantitative research questions

Here are some examples of the types of quantitative research questions to ask participants:

• What is your age/relationship status/income? (multiple-choice covering demographic ranges)

• When using a ____, which of these is most important to you? (multiple-choice questions that cover pricing, support, features, etc)

• When searching for a product, do you use the search field? (Y/N)

Meanwhile, some of the internal questions that quantitative research can answer include: 

• How many users converted after our most recent sale? 

• How are our new email subscriptions trending compared to unsubscribes? 

• How many users are able to complete the sign-up process in under three minutes? 

• Which channel would be most effective for our spring marketing campaign? 

• Does the CTA “learn more” entice more clickthroughs than the CTA “explore”? 

What is qualitative research?

Qualitative research uncovers the underlying motivations, reasons, and contexts that drive quantitative movements. For example, quantitative research may indicate a certain feature of your app is underused, but qualitative research will uncover why. It results in non-numerical data – often quotes from users – gained through in-depth analysis. 

Utilizing open-ended tools like interviews, observations, comments fields, and user diaries, qualitative research can be highly subjective, reflecting not just the opinions and beliefs of users but the interpretations of researchers themselves. 

Embracing this subjectivity is the key to understanding qualitative research. Its primary utility is its ability to reflect real-world conditions and impressions of your product or service, where all the careful internal design work comes into play with real users and use cases. Because the research tends to be more in-depth, it can be centered around a few users – Nielsen Norman Group recommends testing just five users. 

Types of qualitative research

While there are many types of qualitative research, a few are more common than others. 

Interviews

One-on-one conversations with real (or representative) users allow you to gain insights, ask follow-up questions, and probe subjective impressions of a product. 

Focus groups

Group discussions allow you to better understand shared experiences. They can be a more cost-effective way of generating quotes and in-depth impressions than interviewing people individually. 

Ethnographic studies

Despite the academic name, an ethnographic study is really just a fancy way of describing observations in a user’s natural environment. If you’ve designed a home-cooking app, for example, this may be watching a user work in their own kitchen. 

Case studies

These detailed studies zero in on a particular incident to explore in great depth. If you’ve designed healthcare software, this may mean doing a full exploration of how a given provider came to find your product, how they use it in their practice, which internal metrics and processes your software impacted, and what their outstanding needs may be. 

Pros and cons of qualitative research

Qualitative research can be remarkably powerful when you need human insights. Some of its pros include: 

• Flexibility: Qualitative research allows for semi-structured and entirely unstructured research techniques. You can interview people by a loose script or without a script at all, for example. 

• Rich descriptive data: Quotes, personal anecdotes, and detailed narratives help researchers and stakeholders better understand their subject area. 

• Context: By exploring use cases in natural environments or as relayed by real people, you can better understand phenomena in their natural settings. 

Some of the cons to consider include: 

• Time-consuming: Interviewing people or doing in-depth case studies takes more time than simply filing surveys or looking at automated analytics reports. 

• Less generalizability: While you still want quotes or case studies to speak to larger phenomena, qualitative research is generally not intended to be extrapolated to all users, but rather to better reflect the experiences of a subset of users.

• Potential for bias: As mentioned above, researchers’ own subjective experiences can influence how they interpret the research. 

Examples of qualitative research questions

Here are some examples of the types of qualitative research questions to ask participants.:

• Why did ____ happen? (e.g. to understand why a metric went up or down?)

• What do you think/feel/do about ____? When does this happen? 

• How do you understand the message/user interface/process?

• What did you expect to happen when ____? 

• What do you do when you can’t get what you need/want from us? 

• Describe a positive experience with (the product, a process, a service)

• What is most important to you when using this product/service?

• How would you define an ideal ____?

• What do you expect from a ____?

Balancing qualitative and quantitative approaches

Ultimately, your goal isn’t to pick a single winning research method, but to combine qualitative and quantitative techniques to effectively answer internal questions and improve your product. 

Start in terms of the objective of any research project and what the concrete questions are, and move out from there. Here are a few other pointers to keep in mind when balancing methodologies.

Leveraging qualitative and quantitative data at different design phases

Qualitative data can be helpful in the early phases of designing something, to better empathize with users’ needs. This can help conceptualize a product to meet those needs. Later on in development, quantitative data can help you refine your solution.

Post-launch

Once a product has been launched, you may find it helpful to move in the other direction. Quantitative data may indicate an issue with a key metric or feature, but qualitative research will uncover the root cause of that issue, allowing you to develop solutions. Once a solution is in place, you can monitor qualitative data to see if it worked. 

Cross-validate your findings

See if findings from one research method are echoed in another form. If quantitative data indicates users love your mobile app, see if it can be validated in qualitative research. If it can’t, that may point to an issue in the way either method is being conducted. 

Integrate insights into reporting

Whether reporting to stakeholders or other researchers, use quotes to add context to numbers, and vice versa. 

Stay transparent

Clearly document all methodologies and sampling strategies, making clear your rationale to stakeholders and readers. This allows for greater replicability in future research, and allows your methods to be refined over time.

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