What is the difference between nominal and ordinal data?

Nominal data categorises without a specific order, while ordinal data categorises with a meaningful order.

Nominal data is used to label variables without any quantitative value. Think of it as naming or labelling categories that do not have a specific order. For example, if you were to categorise people by their favourite colour, you might have categories like red, blue, green, and yellow. These categories do not have a natural order; one colour is not "greater" or "lesser" than another. Other examples of nominal data include types of pets (dog, cat, fish) or types of cuisine (Italian, Chinese, Mexican).

On the other hand, ordinal data categorises variables in a specific, meaningful order. This type of data not only labels categories but also arranges them in a sequence where the order matters. For instance, consider a survey asking people to rate their satisfaction on a scale of 1 to 5, where 1 means "very unsatisfied" and 5 means "very satisfied." Here, the numbers indicate a ranking or order of satisfaction levels. Another example is educational qualifications, such as GCSE, A-levels, and university degrees, which have a clear progression.

In summary, the key difference lies in the order: nominal data has no inherent order, while ordinal data has a meaningful sequence. Understanding this distinction helps in choosing the right statistical methods to analyse the data effectively.