Generally, there are four data types in statistics: *nominal*, *ordinal*, *interval* and *ratio*.

**Nominal** data as the name suggests is characterize data by name. For example, the categorization of someone as male or female is nominal data. There is no order or rank between nominal data or only difference.

**Ordinal** data is data which can be ordered. For example, student class levels are ordinal in the sense that second year students are above first years students, and third year students are above second year students. Thet may be logical in order but they do not in any way say anything about how good students are or how diferent they are. Third year students, say, are not twice as good as first year students because they are two levels higher than the latter.

**Interval** data has order and also discrete differences in their intervals. Temperature is an example of interval data. There difference of ten degrees between 20 and 30 is equal to the ten degree difference between 30 and 40. They are relative to each other.

**Ratio** data is has an order, discrete intervals and (in Sarah Boslaugh’s word) a “natural” zero. Unlike temperature in the previous example of interval data zero degrees does not end there. Temperatures can drop below zero (and they often do). Weight, height and money and good examples of ratio data in that you cannot be -10 pounds, -10cm or -$10 dollars (well you can be in debt but you can’t show me -$10). You can say your friend has twice the money as yourself.

Just remember, measurement types make most sense when contrasted against each other and not talked about in isolation. When in doubt, try to fit them into the definitions aboves to see which one they match.

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