Know your quantitative data

Preparing data for statistical analysis involves creating a spreadsheet of numerical values. A classic spreadsheet would be one that is completely coded in numbers including things like participant ID, gender, satisfaction scores, or time on task.

In the UX world, quantitative data can come in three flavors: interval, ordinal, and nominal. It is important to recognize each type of data because they determine the stats that you can run.

• Interval data are composed of equally distributed units. Weight is an example of interval data, where everyone weighs a certain number of pounds. Each pound is uniform, so 149 pounds is smaller than 150 pounds in exactly the same way that 150 pounds is smaller than 151 pounds. Common UX interval data include the time that a user requires to complete a task, the number of visitors that a web site has, or a satisfaction scale with every point carefully labeled.
  
  
• Ordinal data are rank ordered but may not be in equally spaced units. Class rank is a common example of an ordinal scale, e.g., every school has students who are ranked 1st, 2nd, 3rd, etc. in their class. However, in School A, students 1, 2, and 3 may have 4.0, 3.9, and 3.4 GPAs, while in School B, the top 3 students might have 3.8, 3.0, and 2.9 GPAs. In the UX world, ordinal data may include user rankings of the five most important features in a product. Satisfaction scales with only a few labeled points are ordinal as well.
  
  
• Nominal data measure discrete categories. The genders of your study participants are nominal data. In a spreadsheet you could code gender as a number, where 0 = male, and 1 = female. You can't do any math directly on the nominal data because it wouldn't make sense. For example, you can't calculate the average of gender even if you coded it as 0s and 1s because there's no sense in reporting that, "On average, the study participants had a gender of 0.6."