the numerical values simply name the attribute uniquely. No ordering of the cases is implied. For example, jersey numbers in basketball are measures at the nominal level. A player with number 30 is not more of anything thna player with number 15 and is certainly not twice whatever number 15 is.
the attributes can be rank-ordered. Here, distances between attributes do not have any meaning. For example, on a survey, you might code educational atainment as 0=less than high school, 1=some high school, 2=high school degree, 3=some college, 4=college degree,5=post college. In this measure, higher numbers man more education. Is distance from 0 to 1 the same as 3 to 4? Of course not. The interval between values is not interpretable in an ordinal measure.
the distance between attributes does have meaning. For example, when we measure temperture (in Fahrenheit), the distance from 30 to 40 is the same as th edistance from 70 to 80. The interval between values is interpretable. Because of this, it makes sense to compute an average of an interval variable,where it doesn’t make sense to do so for ordinal scales. In interval measurement ratios don’t make any sense; 80 degrees is not twice as hot as 40 degrees (although the attribute value is twice as large).
there is always a meaningful absolute zero. This means that you can construct a meaningful fraction (or ratio) with a ratio variable. Weight is a ratio variable. We can ay that a 100 lb bag weighs twice as mucha s a 50 pound one.
The Hierarchy of Measurement Levels
Ration -Absolute Zero
Interval – Distance is Meaningful
Ordinal – Attributes can be ordered.
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