There are four different data types that are used in order to gather and present information:
Nominal data is specifically about categorical, unordered data forms. For example, the types of items you can find in a supermarket including canned, frozen, produce and dairy.
Nominal data can be counted, and the percentage can be calculated, however, you cannot find the average of the data set.
Dichotomous is a type of nominal data where you only have two options, for example, whether or not an item in a supermarket is on sale.
Ordinal data is when the data set is in a specific order, for example: strongly disagree, disagree, neutral, agree, strongly agree. Another example of ordinal data includes choosing an option from a scale of 1-10.
Interval data is where the measurement between two different values is meaningful.
Examples of interval data include calendars, years and temperature. Interval data is where the “0” value has no meaning, for example, 0 degree Celsius doesn’t mean there is an absence of temperature, it just means it’s really cold. The difference between 1.30-2.30pm, is the same as the difference between 2.30-3.30pm.
Ratio data is a lot like meaningful data, where the value of “0” has meaning. For example, there are 0 people in line, there are 0 number of items in your basket.
Data is also distinguished between whether it is qualitative (non-numeric), or quantitative data (numeric):