Qualitative VS Quantitative Data
And the importance of each
Qualitative Data: Information about characteristics that cannot be measured by metrics. For example, how a user feels about a design.
Quantitative Data: Information that can be quantified and verified. For example, how long it takes a user to perform an action.
When I first became a Usability Specialist, I thought I could just get by with surveys sent out in mass. I figured that the results of the survey would convey pretty much how the user felt about a particular subject; while this is correct, I started to realize that users would say one thing in a survey, but continue to do something contrary to their survey results. At this time, I realized that I would need to measure my users’ actions in order to really see why and how they did certain things. Surveys and user testing bring about two different kinds of data, qualitative and quantitative data. Both are beneficial in their own way; however, combined they can show not only how a user interacts with a design but their thoughts regarding the design as well.
The Importance of Qualitative Data
Qualitative data captures the emotions and feelings of a user. While these things cannot be measured numerically through observation, they can give a designer a general idea of a user’s take on a product. Qualitative research brings the designer more to the user’s level, in which the designer can have some context as to what the user may want from their product. It taps much more into human behavior and personality. These things are very important to take into account while creating or testing a product.
The Pros: Qualitative information brings a human touch to the data.
The Cons: Qualitative information can be misleading. Although a user says they feel a certain way, they may continue to act on the contrary. Sometimes the user does not have enough context to explain exactly what they want.
The Importance of Quantitative Data
Quantitative data brings hard facts to the table that cannot be refuted. It can also show the designer trends that users may have; like how they interact with a particular page or element of a page. These trends can lead the designer to make very specific choices in order to either encourage or discourage those trends.
The Pros: Quantitative information brings non-refutable facts.
The Cons: Quantitative information lacks user feedback and opinion.
The Combination of Both
Qualitative and Quantitative both have pros and cons; but, together, they even each other out. When doing a case study or test, both the quantitative and qualitative info can show the whole picture; meaning, you will get both facts and direct user feedback.
While running tests, particularly with web pages, I will do speed tests in order to gain quantitative info; and then following those speed tests I will ask the user questions regarding their experience. Not only does this give me the trends regarding the usability of the page, but it gives me the user’s thoughts and feelings as well. Although this information could be potentially contradicting (for example, a user takes a long time to perform the action but still thinks the page is user-friendly) in most cases it provides useful information. Not only does it show the user’s actions, but their thoughts and feelings as well.
With the combination of both numerical stats and opinions, a designer can have a broader idea of what they can do for a design. Both types of data can lead to a design that fits right in the middle; it can fulfill the user’s requests while meeting the technical needs of the site/page. With this, the users are satisfied with their feedback being listened to, but the flaws in the design that they may not have even thought of are being addressed as well.
Conclusion
Both quantitative and qualitative information is great in their own right, but, together they are even better. Although the types of info can contradict each other depending on what the user says in their feedback, more often than not they strengthen each other and can provide an even bigger picture. Usability is vital to the life of a product; so, why would you limit yourself to only one data type when you can combine them?
Daniel Sanderson is a student in the Digital Media program at Utah Valley University, Orem Utah, studying Interaction & Design. The following article relates to the client web project in the (DGM 2240 Course) and representative of the skills learned.
