Some researchers actually question whether a nominal scale should be considered a "true" scale since it only assigns numbers for the purpose of catogorizing events, attributes or characteristics. The nominal scale does not express any values or relationships between variables. Labelling men as "1" and women as "2" (which is one of the most common ways of labelling gender for data entry purposes) does not mean women are "twice something or other" compared to men. Nor does it suggest that 1 is somehow "better" than 2 (as might be the case in competitive placement).
Consequently, the only mathematical or statistical operation that can be performed on nominal scales is a frequency run or count. We cannot determine an average, except for the mode – that number which holds the most responses - nor can we add and subtract numbers.
Much of the demographic information collected is in the form of nominal scales, for example:
In nominal scale questions, it is important that the response categories must include all possible responses. In order to be exhaustive in the response categories, you might have to include a category such as "other", "uncertain" or "don’t know/can’t remember" so that respondents will not distort their information by trying to forcefit the response into the categories provided. But be sure that the categories provided are mutually exclusive, that is to say do not overlap or duplicate in any way. In the following example, you will notice that "sweets" is much more general than all the others, and therefore overlaps some of the other response categories:
Which of the following do you like: (check all that apply):
|Chocolate o||Pie o|
|Cake o||Sweets o|
|Cookies o||Other o|