SPSS Categories® 11.0

30 Chapter 3
Spline ordinal. The order of the categories of the observed variable is preserved in
the optimally scaled variable. Category points will be on a straight line (vector)
through the origin. The resulting transformation is a smooth monotonic piecewise
polynomial of the chosen degree. The pieces are specified by the user-specified number
and procedure-determined placement of the interior knots.
Spline nominal. The only information in the observed variable that is preserved in the
optimally scaled variable is the grouping of objects in categories. The order of the
categories of the observed variable is not preserved. Category points will be on a
straight line (vector) through the origin. The resulting transformation is a smooth,
possibly nonmonotonic, piecewise polynomial of the chosen degree. The pieces are
specified by the user-specified number and procedure-determined placement of the
interior knots.
Multiple nominal. The only information in the observed variable that is preserved in
the optimally scaled variable is the grouping of objects in categories. The order of the
categories of the observed variable is not preserved. Category points will be in the
centroid of the objects in the particular categories. Multiple indicates that different
sets of quantifications are obtained for each dimension.
Ordinal. The order of the categories of the observed variable is preserved in the optimally
scaled variable. Category points will be on a straight line (vector) through the
origin. The resulting transformation fits better than the spline ordinal transformation
but is less smooth.
Nominal. The only information in the observed variable that is preserved in the optimally
scaled variable is the grouping of objects in categories. The order of the categories
of the observed variable is not preserved. Category points will be on a straight
line (vector) through the origin. The resulting transformation fits better than the
spline nominal transformation but is less smooth.
Numeric. Categories are treated as ordered and equally spaced (interval level). The
order of the categories and the equal distances between category numbers of the observed
variable are preserved in the optimally scaled variable. Category points will
be on a straight line (vector) through the origin. When all variables are at the numeric
level, the analysis is analogous to standard principal components analysis.