User Guide

Chapter
5
Examining S u
mmary Statistics
for Individual Variables
This chapt
er discusses simple summary measures and how the level of measurement
of a variable influences the types of statistics that should be used. We will use the
data file demo.sav.
Level of Measurement
Different summary measures are appropriate for different types of data, depending
on the level of measurement:
Categoric
al.
Data with a limited number of distinct values or categories (for example,
gender or marital status). Also referred to as qualitative data. Categorical variables
can be string (alphanumeric) data or numeric variables that use numeric codes to
represen
t categories (for example, 0 = Unmarried and 1 = Married). There are two
basic types of categorical data:
 Nominal. Categorical data where there is no inherent order to the categories. For
example,
a job category of “sales” isn’t higher or lower than a job category of
“marketing” or “research.”
 Ordinal. Categorical data where there is a meaningful order of categories, but
there isn’t a measurable distance between categories. For example, there is
an order t
o the values high, medium, and low, but the “distance” between the
values can’t be calculated.
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