Let's say there's a measurement hierarchy for discrete variables, that looks like nominal (grade pass/fail) < ordinal (grade A/B/C) < interval (grade 90-100; 80-89; 70-79, etc). Hierarchy is used to refer to the possiblity that we can assign order to the discrete values.
I read a line that methods applicable for one type of variable can be used for the variables at higher levels too, but not at lower levels. Can any one explain and exemplify why this is true (if it is)?
Basically, measurement level describes certain assumption about the variables at hand. These assumptions imply additional information or structure of the data.
For example, take the interval level. Just as at the ordinal level, values are ordered in a certain progression. In programming terms one could say that the interval class inherits the properties of the ordinal class (not to worry if you are unfamiliar with the concept). In other words, the interval class has the same properties as the ordinal, but an additional property is added. Namely, the distance between the "ticks" on the scale is now equal.
So we know that just as on ordinal level 4 is above 2, but we also know that the distance between 0 and 4 is twice as large as the distance between 2 and 4.
Ordinal methods use the information about the order of values. The same information is contained on the interval level. However, by applying ordinal methods to the interval level you waste the information about the homogeneity of distance between values. Still, it is possible.
On the other hand, interval methods rely on the assumption of distance homogeneity. This is why they cannot be (correctly) applied to data which violate that assumption. Still, this is often done e.g. with Likert scales, but this is a whole other discussion.
Hope this helps.
