1.1.1 Overview of Data Types

Categorical Data

• Data which is sorted into groups is considered categorical data

Nominal Data

• • • Categorical data with no hierarchy (i.e. one category is not “greater than” another) is considered nominal data

Example

Eye colour can be considered a nominal data type as the data (each person’s eye colour) can be placed into groups and there is no hierarchy

Ordinal Data

• • • Categorical data which contains a hierarchy is considered nominal data

Example

Height (small, medium, large) can be considered an ordinal data type as the data can be placed into groups and there is a hierarchy (large is greater than medium)

Numerical Data

• Data which represents quantities and can be measured or calculated

Discrete Data

• • • Numerical data which can only take on certain values is considered discrete data

Example

The number of people in a room can be considered a discrete data type as it can only take on whole numbers (1,2,3…) and not decimals

Continuous Data

• • • Numerical data which can take on any value (within reasonable limits) is considered continuous data

Note: continuous data is not necessarily able to take on any value but it can take on any value within a reasonable limit; e.g. height cannot be negative but can take on any positive number (including very small decimal numbers)

Example

Height can be considered a continuous data type as it can take on any positive value

Note: you may have noticed height can be a categorical data type if it is placed into groups (small, medium, large) or a numerical data type if measured or calculated directly. Keep in mind that this can be the case for many types of data.

Classifying Data Flow Chart