Qualitative vs. Quantitative Data

In the user research community the terms qualitative and quantitative are as common as the term UX itself. But often times new designers can get tripped up by the terms. Part of the confusion comes from the various, and sometime confusing applications of the terms. They can refer to the research activity (e.g qualitative study), the data from a research activity (e.g. quantitive data) and sometimes they refer to the analysis of the data from a research activity (quantitive analysis). Its no wonder non-practitioners can get confused. Fortunately, there is a simple way to keep the two concepts straight in your head. If you can grok that the term qualitative is derived from the word quality and the term quantitative is derived from the word quantity, then you’re 90% there. Quantitative data is information that can be represented and evaluated in numerical form. In other words, Quantitative data, derived from the word Quantity, can be counted and objectively measured. Qualitative data is information that is descriptive and anecdotal in nature. In other words, Qualitative data, derived from the word Quality, is subjective and preferential and cannot be objectively measured.

Some additional differences between the two include:

  • the type of questions used to gather data and the answers each provides,
  • the observations styles used for each, and
  • the samples sizes used with each.

The next few section provides some additional details and examples you can use to further distinguish the two concepts.

Research Questions
Most of the time, the type of question you ask a research participant will determine if the data you collect is quantitative or qualitative. Quantitative Data is typically the results of the researcher asking a research participant a set of questions that are closed-ended in nature. Closed-ended questions Closed-ended questions require you participants to answer the questions by either:

  • Selecting one or more value(s) from a given list.
  • Selecting a value from a scale

Some examples of closed ended questions include:

  • Did you enjoy reading the book (yes or no)?
  • Which of the following books are your favorite (Book A, Book B, Book C, None of the above)?
  • On a scale of 1 to 7, with 1 = not at all and 7 = very much, please tell me how much you enjoyed reading the book.

Keep in mind that this is not always the case, but as a rule of thumb, you can rely on this.

Qualitative Data is typically the results of the researcher asking a research participant a set of questions that are Open-ended in nature. Opened ended questions allow the research participant to compose their own answer.

Some examples of closed ended questions include:

  • How did you enjoy reading Book A?
  • What were your favorite parts of Book A?

Again, this is not always the case, but as a rule of thumb, you can rely on this.

Observation Style
Another common difference between Quantitative and Qualitative research is in how the feedback is collected from the participants. Again, as a general rule, Quantitative research uses indirect observations, sometimes through instrumentation to collect customer data. And Qualitative research uses direct observation methods to collect customer data. Indirect observation, simply means the data is not recorded by the research, but instead the data is recorded either by a system or another individual and then reported to research later. A classic example of indirect observation is website analytics data. Through website analytics, a researcher can observe the paths users take through a website to complete a task in a natural environment. Direct observation means the researcher records the data as it is observed. A standard usability study is a classic example of direct observation. Just like the website analytics, the research can observer the path a user takes through a website to complete a task, but in an artificial environment that allow that affords the researcher opportunities to interject with questions that provide insights into the research participant’s decision making process.

Sample Size
One of the primary drivers for the different observation methods is the samples sizes of the two different types of research. Without going into the long technical reasons and evidence for a concept known as statistical significance, I’ll just say that In order for quantitative data to be legitimate, a large enough number of participants need to be tested. The larger the number, the greater the confidence in the outcome of research. Data from large sample sizes are easier to collect through indirect observation. As such, quantitative data is typically collected through indirect observation. It would be too costly and time consuming for a researcher to attempt to collect a significant amount of data through direct observation. Because qualitative research is not based in math, but rather based on anecdotes and descriptions, smaller sample sizes are acceptable.

As you can see, there are a variety of ways to consider, characterize and compare Qualitative and Quantitative research, data and analysis. But at the end of the day, the difference between Qualitative and Quantitative data all comes down to measurement of the data. Quantitative data can be counted and is easy to measure. Qualitative data cannot be counted and as such is more difficult to measure. At least, this is the easiest way to distinguish the two.

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