• Assign numbers that can be used to identify & classify objects. 

• Numbers have no value. 

• Splits data into mutually exclusive categories (dichotomous).

• Ranks data in some order.

• Attributes are ordered. 

• The ranking of the data without establishing the degree of variation between them.

• The order of the values is what’s important and significant, but the differences between each one is not really known. 

Take a look at the example below.  In each case, we know that a #4 is better than a #3 or #2, but we don’t know–and cannot quantify–how much better it is.  For example, is the difference between “OK” and “Unhappy” the same as the difference between “Very Happy” and “Happy?”  We can’t say. 

• Numeric scales in which we know both the order and the exact differences between the values.    

• Differences can be interpreted directly. For example, the difference between 60 and 50 degrees is a measurable 10 degrees. 

• Interval scales not only tell us about order, but also about the value between each item. 

• This scale does not have a “true zero.”  For example, there is no such thing as “no temperature,” at least not with Celsius.  In the case of interval scales, zero doesn’t mean the absence of value, but is actually another number used on the scale, like 0 degrees Celsius. 

• consider this: 10 degrees C + 10 degrees C = 20 degrees C.  No problem there.  20 degrees C is not twice as hot as 10 degrees C, however, because there is no such thing as “no temperature” when it comes to the Celsius scale.  When converted to Fahrenheit, it’s clear: 10C=50F and 20C=68F, which is clearly not twice as hot.