LEVEL Keyword (CATREG command)

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

SPORD. Spline ordinal (monotonic). This is the default for a variable listed without any optimal scaling level, for example, one without LEVEL in the parentheses after it or with LEVEL without a specification. Categories are treated as ordered. The order of the categories of the observed variable is preserved in the optimally scaled variable. Categories will be on a straight line through the origin. The resulting transformation is a smooth nondecreasing piecewise polynomial of the chosen degree. The pieces are specified by the number and the placement of the interior knots.

SPNOM. Spline nominal (non-monotonic). Categories are treated as unordered. Objects in the same category obtain the same quantification. Categories will be on a straight line through the origin. The resulting transformation is a smooth piecewise polynomial of the chosen degree. The pieces are specified by the number and the placement of the interior knots.

ORDI. Ordinal. Categories are treated as ordered. The order of the categories of the observed variable is preserved in the optimally scaled variable. Categories will be on a straight line through the origin. The resulting transformation fits better than SPORD transformation, but is less smooth.

NOMI. Nominal. Categories are treated as unordered. Objects in the same category obtain the same quantification. Categories will be on a straight line 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 differences between category numbers of the observed variables are preserved in the optimally scaled variable. Categories will be on a straight line through the origin. When all variables are scaled at the numerical level, the CATREG analysis is analogous to standard multiple regression analysis.