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Combining Qual and Quant UX Research Methods

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It’s easy to find helpful blogs, tutorials, and books on UX research methods.  If you want to learn how to conduct a solid usability study or card sort study, there are some great references out there.

But, very few resources are talking about one of the most powerful ways to conduct UX research – combining UX research methods to more accurately and completely answer research questions.

While many UX methods can be combined with each other to provide better clarity and understanding of your product’s UX, one of the slam dunk combinations is a qualitative method combined with a quantitative method.

To understand the benefits of a qual/quant combination, it’s necessary to first understand the difference between qual and quant research.

Here’s a high-level summary:

Qualitative Research
Quantitative Research
  • In-depth information from a few users
  • Subjective
  • Exploratory and unstructured
  • Results in descriptive data
  • Uncovers the “why” behind the numbers
  • Used to find themes
  • Examples: usability testing, journey maps, ethnographic interviews
  • Wide breadth of information from numerous users
  • Objective
  • Quantifies defined variables
  • Results in numeric data
  • Uncovers how many and how often
  • Used to validate themes
  • Examples: online surveys, card sort studies, tree jack studies

Some of our most rewarding and successful research projects have been multi-phased research efforts that combined qual and quant methods.   Let me tell you about two of my favorite ways to combine qual and quant data …

1. Persona Development
Qualitative Method Used:  Usability Study & User Journey Map
Quantitative Method Used:  Online Survey

A few years ago, we had a client that wanted to develop personas for the users of their online event planning tool.   Because a simultaneous goal of the project was to assess the usability of the tool, we began the project with a round of qualitative sessions.  We traveled to two cities and invited 16 target users into the lab to help us create a journey map of their current event planning process and to usability test our event planning tool.  During the session, we made sure to capture ample details about the users’ background and their goals and needs during the planning process as well as to learn about a recent even each user planned.  From this data, we identified three user groups or personas.  We’ll call them the Social Entertainer, the Activist, and the Event Professional.

It would have been tempting to end the research after this study.   Our personas made sense and fit our data.  But, we didn’t have a large enough sample size to be confident that our personas adequately represented the larger population of online event planners.  We also didn’t have enough data to know what percentage of users fit into each persona.  Was the online tool being used more by soccer moms or weekend warriors?   So, our next step was to conduct an online survey.  The survey was designed to capture data that would either confirm our persona groups or identify new groups.   We got over 200 survey responses and eagerly analyzed the data.  We found that our original persona definitions were close, but not fully accurate.  The survey data also allowed us to better quantify the top needs and goals of each persona group.  From this data we altered our persona definitions slightly and renamed the Activist persona to the Community Volunteer, which better described this user group. The survey data also quantified the size of each persona in the total event planning population.

In a world of endless time and budget, it would have been great to follow-up the online survey with another small round of qualitative research that focused on getting to know our fourth persona more deeply.  However, the existing qual and quant data was helpful in defining this group.

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2. Next Generation Innovation

Quantitative Method Used: Online Survey

Qualitative Method Used:  User Interviews / Usability Testing

We’ve worked on several projects for clients who have an existing product on the market and are beginning work on their next generation product which will include new features while reducing current UX issues.  One helpful way to start these projects is by surveying existing users to understand how they are using the product, what they like about the product, and what could be improved.  By starting with the survey first, a wide net is cast and responses are captured from a large sampling of users across user groups.  In this case, we’re more likely to hear about and can quantify the variability of current product uses, likes, and dislikes.

Once we know the top product users, likes and dislikes, it’s usually an easy exercise to determine which findings are worthy of in-depth qualitative research.

As an example, suppose that the survey data revealed that that only very few users are using one of the product’s key features.  The product team would probably want to know why.   Is it that users don’t know the feature exists or that they don’t consider it a useful feature or that it has too many painful usability issues?   This could be a enlightening area of focus during qualitative testing.

Another survey finding could be that users rate a certain feature as difficult to use.  Open-ended responses might provide more insight about what, in particular, is hard to use, but a focused usability study on this feature will yield far greater detail and understanding of users’ frustration so that the feature can be more successfully redesigned to meet user needs.

In Summary

When UX data is to inform important business decisions, it is crucial that the data tells the whole story.  There are many research questions that are best answered by a combination of qualitative and quantitative data.  Knowing which method to conduct first, qual or quant, will depend on the goals of your research, what you already know about your product’s UX, and how the data will be used.


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