What is qualitative data in statistics?
Qualitative is defined as information that describes or categorizes something. It answers the broad question of “What qualities does this have?”
Qualitative data cannot be easily measured or counted and therefore often doesn’t contain numbers. For example, you might interview customers to determine which social media platform they use most. You would then categorize the responses by platform such as Facebook, Twitter, Quora, Snapchat, etc.
Or an ecommerce retailer may poll shoppers to see which color - teal, gray, or white - is preferable for a specific item. (Note: if you combine all the results from the poll - e.g. 45 teal, 70 gray, and 52 white, this becomes quantitative data.)
In some instances, a number or code may be assigned to qualitative descriptions or categories. For example, a company may assign numbers 1-5 to a satisfaction survey: Very satisfied (5), Satisfied (4), Somewhat satisfied (3), Somewhat dissatisfied (2), and Dissatisfied (1). (You might be wondering, does this turn it into quantitative data? That’s a great question with a complicated answer. You can learn more here about the debate on this type of data - ordinal data.)
Because qualitative data describes, it’s often subjective and relative such as cheap, expensive, smaller, larger, sweet, sour, highly engaged, disengaged, etc.
It’s worth noting that most people use the term ‘qualitative data’ more loosely in business than the pure statistical definition (above). The more general business use refers to user interviews or research - rich information that cannot be measured.
Types of qualitative data
There are three types of qualitative data: binomial data, nominal data, and ordinal data.
- Binomial data (or binary data): this divides information into two mutually exclusive groups. Examples of binary data are true/false, right/wrong, accept/reject, etc.
- Nominal data (or unordered data): this groups information into categories that do not have implicit ranking. Nominal data examples include colors, genres, occupations, geographic location, etc.
- Ordinal data (or ordered data): as the name implies, information is categorized with an implied order. Examples of ordinal data are small/medium/large, unsatisfied/neutral/satisfied, etc.
What’s the difference between quantitative and qualitative data?
The terms quantitative and qualitative data are often mentioned together, so it’s important to understand the distinction between the two.
Qualitative data is information that describes or categorizes. This involves qualities.
Quantitative data is information that measures or counts. This involves numbers such as monthly revenue, distance of a race and time of the winner, calories in a meal, temperature, or salary. There’s a more complete definition of quantitative data here (including examples).