Quantitative versus qualitative data

To accomplish our diagnoses of the various types of data, we will begin with the highest order of separation. When dealing with structured, tabular data (which we usually will be doing), the first question we generally ask ourselves is whether the values are of a numeric or categorical nature.

Quantitative data are data that are numerical in nature. They should be measuring the quantity of something.

Qualitative data are data that are categorical in nature. They should be describing the quality of something.

Basic examples:

• Weather measured as temperature in Fahrenheit or Celsius would be quantitative
• Weather measured as cloudy or sunny would be qualitative
• The name of a person visiting the White House would be qualitative
• The amount of blood you donate at a blood drive is quantitative

The first two examples show that we can describe similar systems using data from both the qualitative and quantitative side. In fact, in most datasets, we will be working with both qualitative and quantitative data.

Sometimes, data can, arguably, be either quantitative or qualitative. The ranking you would give a restaurant (one through five stars) could be considered quantitative or qualitative, for example. While they are numbers, the numbers themselves might also represent categories. For example, if the restaurant rating app asked you to rate the restaurant using a quantitative star system, then feasibly the restaurant's average ranking might be a decimal, like 4.71 stars, making the data quantitative. At the same time, if the app asked you if you hated it, thought it was OK, liked it, loved it, or really loved it, then these are now categories. As a result of these ambiguities between quantitative and qualitative data, we employ an even deeper method called the four levels of data. Before we do that, let's introduce our first dataset for the chapter and really solidify some examples of qualitative and quantitative data.