This week’s lecture pod introduced us to the four main data types;

- nominal – consists of named categories to which the data fall. Usually unordered, can be counted but cannot be used to calculate the average.

Example:

- ordinal – numbers are used to categorise data but do not hold any mathematical value. We can use any set of ordered number to represent groups.

Example:

- interval – interval data is numeric and we can perform mathematical operations with the data. something to remember is that the value 0 does not mean the absence of data. rather, it represents the starting point of the data.

Example: 00:00 means 12 o’clock midnight, or the start of a new day, not the absence of time.

- ratio – very similar to interval data, except that 0 DOES mean the absence of data.

Example: the measurements of height, weight, age, money.

In summation, I can appreciate the systematic approach to categorising data. It prevents confusion and simplifies the the visualisation process.

I do agree that nominal data can be blurred and can fall into numerical data, but I think that depends on the type of information the designer is trying to visualise. If the information relies on numbers to tell it’s story, then it becomes numerical. otherwise, it remains categorical.

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