Nominal classifies data into mutually exclusive (nonover-lapping) categories in which no order or ranking can be imposed on the data
Key: Nominal --> "Names"
Example(s): A sample of college instructors classified according to subject taught (e.g., English, history, psychology, or mathematics) is an example of nominal-level measurement. Classifying survey subjects as male or female is another example of nominal-level measurement. No ranking or order can be placed on the data. Classifying residents according to zip codes is also an example of the nominal level of measurement. Even though numbers are assigned as zip codes, there is no meaningful order or ranking. Other examples of nominal-level data are political party (Democratic, Republican, Independent, etc.), religion (Christianity, Judaism, Islam, etc.), and marital status (single, married, divorced, widowed, separated)..
Ordinal classifies data into categories that can be ranked; however, precise differences between the ranks do not exist.
Key: Ordinal --> "Order"
Example(s): from student evaluations, guest speakers might be ranked as superior, average, or poor. Floats in a homecoming parade might be ranked as first place, second place, etc. Note that precise measurement of differences in the ordinal level of measurement does not exist. For instance, when people are classified according to their build (small, medium, or large), a large variation exists among the individuals in each class. Other examples of ordinal data are letter grades (A, B, C, D, F).