Variable measurement level

You can specify the level of measurement as scale (numeric data on an interval or ratio scale), ordinal, or nominal. Nominal and ordinal data can be either string (alphanumeric) or numeric.

A variable can be treated as nominal when its values represent categories with no intrinsic ranking (for example, the department of the company in which an employee works). Examples of nominal variables include region, postal code, and religious affiliation.
A variable can be treated as ordinal when its values represent categories with some intrinsic ranking (for example, levels of service satisfaction from highly dissatisfied to highly satisfied). Examples of ordinal variables include attitude scores representing degree of satisfaction or confidence and preference rating scores.
A variable can be treated as scale (continuous) when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. Examples of scale variables include age in years and income in thousands of dollars.
Note: For ordinal string variables, the alphabetic order of string values is assumed to reflect the true order of the categories. For example, for a string variable with the values of low, medium, high, the order of the categories is interpreted as high, low, medium, which is not the correct order. In general, it is more reliable to use numeric codes to represent ordinal data.
Table 1. Rules for determining measurement level
Condition Measurement Level
All values of a variable are missing Nominal
Format is dollar or custom-currency Continuous
Format is date or time (excluding Month and Wkday) Continuous
Variable contains at least one non-integer value Continuous
Variable contains at least one negative value Continuous
Variable contains no valid values less than 10,000 Continuous
Variable has N or more valid, unique values* Continuous
Variable has no valid values less than 10 Continuous
Variable has less than N valid, unique values* Nominal

* N is a user-specified cut-off value. The default is 24.

  • You can change the cutoff value in the Options dialog. See the topic Data Options for more information.
  • The Variable information pane, available when you click a variable heading, can help you assign the correct measurement level. See the topic Assigning the Measurement Level for more information.

Specifying the measurement level

  1. Open a dataset and make the Variables tab the active tab.
  2. Select a variable measurement level from the list in the Measure field.
    Note: The updates take effect immediately, but are not permanent until you save the dataset (File > Save or File > Save as...).