There are four different data types of measured variables:

Nominal

Nominal data (also known as qualitative/categorical data) is data that is split into categories.

For example: what kind of data would you collect for the variable "Color"? You would end up with information such as "red", "green", "blue", and so on. This qualitative information is called nominal data.

Ordinal

Ordinal data is data where order matters, but distance between values does not.

For example: imagine three people in a race. One finishes in 1st place, one in 2nd place, and the last in 3rd place. This data can be placed in order, but we can’t necessarily measure the distance between values (maybe 1st place finished four seconds ahead of 2nd place, and 2nd place finished nineteen seconds ahead of 3rd place).

Interval

Interval data is data where order matters, and distances between values are equal and meaningful, and a natural zero is not present.

For example: temperature (in Fahrenheit or Celcius) is interval data. The difference between 10 degrees and 20 degrees is 10 degrees. The difference between 80 degrees and 90 degrees is 10 degrees. The scale at any given point is constant, while a measurement of 0 degrees does not reflect a true "lack of temperature".

Ratio

Ratio data is data where order matters, distances between values are equal and meaningful, and a natural, meaningful zero is present.

For example: mass is ratio data. The difference between 140 grams and 155 grams is 15 grams. The difference between 280 grams and 295 grams is 15 grams. The scale at any given point is constant, and a measurement of 0 reflects a complete lack of mass.

Why does it matter? Different types of data allow for different types of data analysis.