Scale levels

A distinction is made between the following scale levels:

Nominal scale: In a nominal scale, different numbers simply represent different attributes. Which attribute is assigned to which number is unimportant, as the numbers do not stand for "more" or "less", "greater" or "smaller".
Examples: question on the field of study (1 = mathematics, 2 = information technology, etc.) or gender (1 = male, 2 = female) in a questionnaire.

Analysis options: Frequency distributions .

Ordinal scale: In an ordinal scale, the numbers represent a rank order; however the scale does not provide any information as to the relations of the attributes in the rank order. The same intervals between the values do not therefore mean the same intervals as "in reality".
Example: rank order of the best five students in a quiz.

Analysis possibilities: Frequency distributions, analysis of rank information.

Interval scale: In an interval scale the numbers represent equal increments of the attribute being measured, but there is no "real" zero point.
A response scale of a questionnaire, for example, ranging from 1 (does not correspond at all) to 7 (corresponds fully) is usually interpreted as an interval scale.

Analysis possibilities: Frequency distributions, analysis of rank information, differences, totals, mean values.

Ratio scale: In a ratio scale, the ratio of the intervals between the attributes is relevant. By contrast to interval scale, the scale has a meaningfully interpretable zero point.
Example: Age, number of specific actions in a log file.

Analysis possibilities: Frequency distributions, analysis of rank information, differences, totals, mean values.