Chapter 2 Variable Types

We examine the most common Variable Types in this section. This is meant to introduce you to the various types of variables which will be used for Exploratory Data Analysis. This is not an exhaustive and complete set of variable types.

The following explanation has been taken from the Australian Bureau of Statistics

2.1 Numeric Variables

Numeric variables have values that describe a measurable quantity as a number, like ‘how many’ or ‘how much’. Therefore numeric variables are quantitative variables.

A continuous variable is a numeric variable. Observations can take any value between a certain set of real numbers. The value given to an observation for a continuous variable can include values as small as the instrument of measurement allows. Examples of continuous variables include height, time, age, and temperature.

A discrete variable is a numeric variable. Observations can take a value based on a count from a set of distinct whole values. A discrete variable cannot take the value of a fraction between one value and the next closest value. Examples of discrete variables include the number of registered cars, number of business locations, and number of children in a family, all of of which measured as whole units (i.e. 1, 2, 3 cars).

The data collected for a numeric variable are quantitative data.

2.2 Categorical Variables

Categorical variables have values that describe a ‘quality’ or ‘characteristic’ of a data unit, like ‘what type’ or ‘which category’. Categorical variables fall into mutually exclusive (in one category or in another) and exhaustive (include all possible options) categories. Therefore, categorical variables are qualitative variables and tend to be represented by a non-numeric value.

Categorical variables may be further described as ordinal or nominal:

An ordinal variable is a categorical variable. Observations can take a value that can be logically ordered or ranked. The categories associated with ordinal variables can be ranked higher or lower than another, but do not necessarily establish a numeric difference between each category. Examples of ordinal categorical variables include academic grades (i.e. A, B, C), clothing size (i.e. small, medium, large, extra large) and attitudes (i.e. strongly agree, agree, disagree, strongly disagree).

A nominal variable is a categorical variable. Observations can take a value that is not able to be organised in a logical sequence. Examples of nominal categorical variables include sex, business type, eye colour, religion and brand.