Variable
Definition
A variable is any characteristic, number, or quantity that can be measured or
counted. A variable may also be called a data item. Age, sex, business income and
expenses, country of birth, capital expenditure, class grades, eye colour and vehicle
type are examples of variables. It is called a variable because the value may vary
between data units in a population, and may change in value over time.
For example; 'income' is a variable that can vary between data units in a population
(i.e. the people or businesses being studied may not have the same incomes) and can
also vary over time for each data unit (i.e. income can go up or down).
Types of variables
There are different ways variables can be described according to the ways they can be
studied, measured, and presented.
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.
Numeric variables may be further described as either continuous or discrete:
• 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.
Here are some simple examples of continuous variables:
1. Weight: It can be measured in kilograms, pounds, or grams and can take any
value, like 55.5 kg or 120.8 lbs.
2. Temperature: It can be recorded in degrees Celsius or Fahrenheit, such as
22.4°C or 72.8°F.
3. Time: It can be measured in seconds, minutes, or hours, such as 3.5 hours or
120.75 seconds.
4. Distance: It can be measured in meters, miles, or kilometers, like 5.3 miles or
10.25 kilometers.
• 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).
Here are some simple examples of discrete variables:
1. Number of students in a class: This can only take whole number values like 25,
30, or 45.
2. Number of cars in a parking lot: The count can be 0, 10, 20, etc., but not 10.5.
3. Number of siblings someone has: Possible values are whole numbers like 0, 1,
2, or 3.
4. Number of books on a shelf: It could be 3, 10, or 15, but not 3.2.
The data collected for a numeric variable are quantitative data.
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).
Examples of ordinal variables, which are categorical variables with a meaningful order
or ranking, include:
1. Education level: Categories such as "high school," "associate's degree,"
"bachelor's degree," "master's degree," "Ph.D."
2. Customer satisfaction rating: Levels like "very dissatisfied," "dissatisfied,"
"neutral," "satisfied," "very satisfied."
3. Pain severity: Descriptors such as "no pain," "mild," "moderate," "severe."
4. Socioeconomic status: Categories like "low income," "middle income," "high
income."
5. Military rank: Levels such as "private," "corporal," "sergeant," "lieutenant,"
"captain."
• 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.
Gender: Categories such as "male," "female," "non-binary."
Marital status: Categories like "single," "married," "divorced," "widowed."
Blood type: Types such as "A," "B," "AB," "O."
Hair color: Categories like "blonde," "brunette," "red," "black."
Country of origin: Categories such as "United States," "Canada," "Mexico,"
"Germany."
The data collected for a categorical variable are qualitative data.
https://www.abs.gov.au/statistics/understanding-statistics/statistical-terms-and-
concepts/variables#:~:text=A%20variable%20is%20any%20characteristic,be%20called%20a%20data%20item
