Quantitative data is countable and numbers-based. Quantitative data allows us to answer questions about how much of, or how often, something happens.
In a customer satisfaction survey, there are typically questions that are measured on a scale and are therefore quantitative (e.g., Rate your experience from 1 to 10). There are also frequently questions that ask for comments. These questions produce qualitative data.
Subcategories of Quantitative Data
Ordinal
The values occur in an order. The order could be numeric, like a rating scale from one to 10. Or the scale might be stated in values, such as Strongly agree to Strongly Disagree. The latter are often converted to numeric values to calculate statistics. The distance between two measures on an ordinal scale may or may not be exactly the same. On an ordinal scale from Extra small to extra large, the difference between large and extra large may not be the same as the difference between small and extra small.
Interval
Interval measures are a type of ordered measure, but they are ordered with precise and consistent differences between each point in the measurement spectrum. Temperature is an example: the difference between five and six degrees celsius will be the same as the difference between 44 and 45. But for Intervals, there’s no real zero point. That means, for example, that we can’t say that 20 degrees is twice as warm as 10.
Ratio
Ratio measures are like interval measures, but have precise zero values that can be measured. You can compare ratio measures by saying, for example, this car is twice as expensive as that other car. Other examples include speed. A car can be stopped and therefore moving with zero speed. Fifty miles per hour is twice as fast as 25 and half as fast as 100.
