User Guide

79
Data Editor
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
(alphanume
ric) or numeric. Measurement specification is relevant only for:
 Custom Tab
les procedure and chart procedures that identify variables as scale or
categorical. Nominal and ordinal are both treated as categorical. (Custom Tables
is available only in the Tables add-on component.)
 SPSS-format data files used with AnswerTree.
You can select one of three measurement levels:
Scale. Data values are numeric values on an interval or ratio scale—for example, age
or income.
Scale variables must be numeric.
Ordinal. Data values represent categories with some intrinsic order (for example,
low, medium, high; strongly agree, agree, disagree, strongly disagree). Ordinal
variables
can be either string (alphanumeric) or numeric values that represent distinct
categories (for example, 1 = low,2=medium,3=high).
Note: For ordinal string variables, the alphabetic order of string values is assumed
to reflec
t 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 c
odes to represent ordinal data.
Nominal. Data values represent categories with no intrinsic order—for example, job
category or company division. Nominal variables can be either string (alphanumeric)
or numer
ic values that represent distinct categories—for example, 1 = Male,2=
Female.