# Selecting the Optimal Scaling Level

It is important to understand that there are no intrinsic properties of a variable that automatically predefine what optimal scaling level you should specify for it. You can explore your data in any way that makes sense and makes interpretation easier. By analyzing a numerical-level variable at the ordinal level, for example, the use of a nonlinear transformation may allow a solution in fewer dimensions.

The following two examples illustrate how the "obvious" level of measurement might not be the best optimal scaling level. Suppose that a variable sorts objects into age groups. Although age can be scaled as a numerical variable, it may be true that for people younger than 25 safety has a positive relation with age, whereas for people older than 60 safety has a negative relation with age. In this case, it might be better to treat age as a nominal variable.

As another example, a variable that sorts persons by political preference appears to be essentially nominal. However, if you order the parties from political left to political right, you might want the quantification of parties to respect this order by using an ordinal level of analysis.

Even though there are no predefined properties of a variable that make it exclusively one level or another, there are some general guidelines to help the novice user. With single-nominal quantification, you don't usually know the order of the categories but you want the analysis to impose one. If the order of the categories is known, you should try ordinal quantification. If the categories are unorderable, you might try multiple-nominal quantification.