Types of Statistical Data Variables
As the study of data, it is useful in statistics to think of different types of data in differently relevant ways. Variables are simply labels we give to datasets containing the same quantity measured in the same context, and therefore possessing the same qualities and attributes. A variable is therefore simply an abstraction to think of several data samples as a single entity.
There are different kind of variables, and it is useful to label these categorisations so to be aid ourselves in reasoning about them at a later stage. In general, we have
- Categorical/Nominal Data Variables: these are used to refer to data that is grouped in particular categories, groups, or bins. Typically categorical data describes something’s characteristics. While they can be represented numerically, the individual numbers have no mathematical meaning. Binary data is an example of categorical data (e.g. are you male or female?).
- Ordinal Data Variables: these essentially refer to categorical data where the ordering of the categories matters. For example describing someone’s education level (e.g. elementary, high school, undergraduate, graduate).
- Numerical Data Variables: these are used to refer to data whose value is intrinsically tied to the phenomenon being measured. Numerical data can either be
- discrete: discrete data is data that can only take on certain values.
- continuous: continuous data is data that cannot be counted.
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