Data Measurement Techniques

Nominal Level

The lowest level of data measurement is the Nominal Level. Numbers representing nominal level data can be used only to classify or categorize. The numbers are used only to differentiate or categorize items or persons but not to make value statement about them. Many demographic questions in surveys result in data are nominal because the questions are used for classification only. Examples of nominal level data are as follows:

Gender, Religion, Ethnicity, Geographical Location, Place  of Birth, Employee Ids, Student Roll Numbers, Telephone Numbers, Security Numbers etc.

Statistical techniques that are appropriate for analyzing nominal data are limited and that is Chi-Square analysis.

Ordinal Level

Ordinal level data is higher than the nominal level. In addition to nominal level capabilities, ordinal level can be used to rank or order objects. For example, using ordinal data, a supervisor can evaluate three employees by ranking their productivity with the number 1 through 3. The supervisor could identify one employee as the most productive, one as least productive and one as somewhere between by using ordinal level data. However, the supervisor could not say the exact difference in the amount of productivity between the employees ranked 1, 2 and 3.

Because nominal and ordinal data are often derived from measurements such as demographic questions, the categorization of people or objects or the ranking of items, nominal and ordinal data are Non-Metric data and sometimes referred to as Qualitative Data.

Interval Level

Interval level data measurement is the next to the highest level of data in which the distances between consecutive numbers have meaning and the data are always numerical. The differences between consecutive numbers are always equal in interval level data means interval data have equal intervals. Interval level data is not having a Zero as fixed point and zero does not mean the absence of the characteristic. For example, zero degree temperature does not mean absence of heat or cold.

Ratio Level

Ratio level data measurement is the highest level of data measurement. Ratio data have the same properties as interval data, but ratio data have an absolute zero and that is fixed and that means absence of the characteristic being studied. The value of zero cannot be arbitrarily assigned because it represents a fixed point. Examples are height, weight, time, volume etc.