SPSS Categories® 11.0

2 Chapter 1
While adaptations of most standard models exist specifically to analyze categorical
data, they often do not perform well for data sets that feature:
Too few observations
Too many variables
Too many values per variable
By quantifying categories, optimal scaling techniques avoid problems in these situations.
Moreover, they are useful even when specialized techniques are appropriate.
Rather than interpreting parameter estimates, the interpretation of optimal scaling
output is often based on graphical displays. Optimal scaling techniques offer excellent
exploratory analyses, which complement other SPSS models well. By narrowing the
focus of your investigation, visualizing your data through optimal scaling can form the
basis of an analysis that centers on interpretation of model parameters.
Optimal Scaling Level and Measurement Level
This can be a very confusing concept when you first use Categories procedures. When
specifying the level, you specify not the level at which variables are measured, but the
level at which they are scaled. The idea is that the variables to be quantified may have
nonlinear relations regardless of how they are measured.
For Categories purposes, there are three basic levels of measurement:
The nominal level implies that a variable’s values represent unordered categories.
Examples of variables that might be nominal are region, zip code area, religious affiliation,
and multiple choice categories.
The ordinal level implies that a variable’s values represent ordered categories. Examples
include attitude scales representing degree of satisfaction or confidence and
preference rating scores.
The numerical level implies that a variable’s values represent ordered categories
with a meaningful metric, so that distance comparisons between categories are appropriate.
Examples include age in years and income in thousands of dollars.
For example, suppose the variables region, job, and age are coded as shown in Table 1.1.
Table 1.1 Coding scheme for region, job, and age
Region Job Age
1 North 1 intern 20 twenty years old
2 South 2 sales rep 22 twenty-two years old
3 East 3 manager 25 twenty-five years old
4 West 27 twenty-seven years old