Quantitative vs. qualitative research

Sylvia Kidder, Ph.D.

Sylvia Kidder, Ph.D.

Senior Insights Analyst

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Applying the right research methods to create digital tools your customers will love.

In this article, you’ll learn about the different types of research methods, how they enhance your user and customer experiences, and how you can apply these methods, even if you don’t have a research team.

Investing in user research is critical to ensure a digital product’s success, both early in product development and continuously throughout the product lifecycle. During the research phase of product development, it can be difficult to know which method (or methods) to utilize, given that there are many, and each has advantages and disadvantages. Both can be valuable, depending on the business need, existing knowledge, and available resources.

Quantitative vs. qualitative research

Quantitative research

Involves collecting numeric data, usually to test specific hypotheses and get results that can be generalized to a larger population.

Tells us:

The ‘what’ and ‘who’

Qualitative research

Does not rely on numbers and is more commonly used to generate new ideas or to richly characterize a specific situation or perspective.

Tells us:

The ‘why’ and ‘how’

A Quick Note: Data vs. Analysis

It’s important to differentiate between data and analysis within a research project. Data are just pieces of information that have been recorded. Analysis is an action or the gathering and analyzing information. Both data and analysis can be quantitative or qualitative.

Normally, quantitative data are analyzed quantitatively, and qualitative data are analyzed qualitatively, but qualitative data can also be analyzed qualitatively and vice versa (confusing we, know). For example, a ‘quantitative content analysis’ of qualitative interview data involves counting the number of times certain words or themes arise in the transcripts.

When should you use each method?

Let’s say a retailer wants to improve their online shopping experience. One way to start is by surveying your current customers to determine the extent to which they are satisfied with specific features of your website and the overall experience. The results could help guide how certain product features should be prioritized.

But what if you are trying to expand your customer base and aren’t sure how to start? Conducting interviews with potential customers can help understand their needs and what would motivate them to choose one retailer over another.

Key point #1:

Quantitative doesn’t equal ‘objective.’

There is a widespread perception that quantitative research is more objective, or truer than qualitative research because numbers are exact and often easier to grasp. For example, it is more specific and impactful to say, “82% of respondents said they would purchase our product” than to say, “The majority of people we talked to said they would purchase our product.” 

However, precision and accuracy are not the same thing. Insights generated from quantitative research are only as good as the underlying data and analysis. Researchers must carefully consider: 

  • The sample (i.e., the group of people participating in the research)
  • The types of data collected (i.e., how you measure what you want to know)
  • The method of data collection
  • The way data is analyzed

One famous example of bad research is the 1936 Literary Digest poll of 10 million Americans that caused them to incorrectly declare the U.S. presidential election for Landon instead of Roosevelt. Even more embarrassingly, a Gallup poll a mere fraction of the size returned an accurate prediction because it utilized better methods.

When quantitative research goes wrong

The idea of “garbage in, garbage out” applies to data. In fact, results gained from bad tools or inappropriate analyses can be worse than no results at all. 

Even seemingly basic “rules” of research, such as that surveying more people make your results more accurate, can be wrong when poorly implemented. 

The last stages of research, interpretation and presentation, can also create bias and inaccuracy in even the most robust quantitative research. “Inconvenient” results can be glossed over, color and visual elements can be used to evoke certain emotions, and visualizations can be tweaked to tell a particular story. One of the biggest perpetrators of this is manipulation of scale (e.g., stretching charts to make little differences look big) and conclusions based on visuals without understanding the numbers in context. 

Image of two graphs demonstrating how quantitative research can provide precision

While quantitative research is ideal when you already have ideas you want to test and need precision in the results, it is easy for bad quantitative research to masquerade as high quality.

You can trust qualitative research

The flip side of the previous point is that qualitative research is “too squishy” to be trusted. While it’s true that qualitative research can be left to interpretation, the methods for analyzing the information are designed to stay grounded in the data. This interpretative component makes qualitative research ideal for understanding context, gaining insight, and generating new ideas.

Subjectivity, rather than being a negative attribute, is actually a unique strength of qualitative research. The goal in consumer research is often to understand consumers’ views of themselves and the world, which can vary wildly from an outsider’s perspective. 


Imagine you are designing a marketing campaign for your outdoor sports gear company. You could take a quantitative approach and find out how often your target customer spends time doing outdoor activities like hiking, skiing, or biking. However, it would probably be more fruitful to focus on understanding how your customers see themselves and their activities.

You may find opportunity, for example, in broadening your campaign to appeal to the couch potato who fancies himself an outdoorsman and dreams of adventure. Qualitative methods allow you to gain a deeper, more flexible understanding of your customers.

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Key point #2:

Quantitative research results can be engaging.

Those of us on the Insights and Analytics team at Nerdery find numbers beautiful, but we realize that not everyone feels that way. Many people’s eyes glaze over when presented with pages of Excel sheets and statistics. A skilled analyst can turn the results of quantitative research into a compelling story, frequently with the help of some thoughtful data visualizations.

For data that will be collected on an ongoing basis, like website statistics or internal metrics, data dashboards can make it easy to digest information and spot trends. They can feature gorgeous, presentation-ready graphics or be designed for serious drilling down, depending on business needs.

Apart from presentation, quantitative research can be creative and entertaining at times.

For example, ‘garbologists’ study refuse to learn about consumption habits. Garbage may be quantified, for example, as pounds of food waste produced or as tallies of cans and boxes.

One garbology study found that people underreported in surveys the amount of beer they drank, demonstrating the utility of this creative approach. Nerdery specializes in digging through data rather than dumpsters (please don’t ask us to sort literal garbage), but we won’t hesitate to utilize nontraditional methods if needed.

It plots existing data and models and presents them in a more easy-to-understand format through charts, graphs, maps and other illustrative formats. 

To make viewing data more relevant for different audiences, scientists and architects will create custom dashboards based on the organization’s role and level of access. 

Where do you start?

A comparison of quantitative and qualitative research methods highlights the value of both approaches for generating useful knowledge. They can guide the design and development of digital products by ensuring alignment with user needs, preferences, and expectations. By collaborating with our experts, you can enhance the quality and depth of your research, ultimately leading to more robust and actionable findings.

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