Level Keyword (CATPCA command)

The following keywords are used to indicate the optimal scaling level:

SPORD. Spline ordinal (monotonic). This setting is the default. The order of the categories of the observed variable is preserved in the optimally scaled variable. Category points will lie 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.

SPNOM. Spline nominal (nonmonotonic). 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 lie 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.

MNOM. 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.

ORDI. Ordinal. The order of the categories on the observed variable is preserved in the optimally scaled variable. Category points will lie on a straight line (vector) through the origin. The resulting transformation fits better than SPORD transformation but is less smooth.

NOMI. 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 lie on a straight line (vector) through the origin. The resulting transformation fits better than SPNOM transformation but is less smooth.

NUME. Numerical. Categories are treated as equally spaced (interval level). The order of the categories and the equal distances between category numbers of the observed variables are preserved in the optimally scaled variable. Category points will lie on a straight line (vector) through the origin. When all variables are scaled at the numerical level, the CATPCA analysis is analogous to standard principal components analysis.