SPSS® 12.0 Command Syntax Reference

Overview
Options
ALSCAL 101
ALSCAL uses an alternating least-squares algorithm to perform multidimensional scaling
(MDS) and multidimensional unfolding (MDU). You can select one of the five models to
obtain stimulus coordinates and/or weights in multidimensional space.
Data Input. You can read inline data matrices, including all types of two- or three-way data,
such as a single matrix or a matrix for each of several subjects, using the INPUT subcommand.
You can read square (symmetrical or asymmetrical) or rectangular matrices of proximities
with the SHAPE subcommand and proximity matrices created by PROXIMITIES and CLUS-
TER with the MATRIX subcommand. You can also read a file of coordinates and/or weights to
provide initial or fixed values for the scaling process with the FILE subcommand.
Methodological Assumptions. You can specify data as matrix-conditional, row-conditional, or
unconditional on the CONDITION subcommand. You can treat data as nonmetric (nominal or
ordinal) or as metric (interval or ratio) using the LEVEL subcommand. You can also use
LEVEL to identify ordinal-level proximity data as measures of similarity or dissimilarity and
can specify tied observations as untied (continuous) or leave them tied (discrete).
Model Selection. You can specify most commonly used multidimensional scaling models by
selecting the correct combination of ALSCAL subcommands, keywords, and criteria. In
addition to the default Euclidean distance model, the MODEL subcommand offers the
individual differences (weighted) Euclidean distance model (INDSCAL), the asymmetric
Euclidean distance model (ASCAL), the asymmetric individual differences Euclidean
distance model (AINDS), and the generalized Euclidean metric individual differences model
(GEMSCAL).
Output. You can produce output that includes raw and scaled input data, missing-value
patterns, normalized data with means, squared data with additive constants, each subject’s
scalar product and individual weight space, plots of linear or nonlinear fit, and plots of the
data transformations using the PRINT and PLOT subcommands.
Basic Specification
The basic specification is VARIABLES followed by a variable list. By default, ALSCAL
produces a two-dimensional nonmetric Euclidean multidimensional scaling solution. Input
is assumed to be one or more square symmetric matrices with data elements that are
dissimilarities at the ordinal level of measurement. Ties are not untied, and conditionality is
by subject. Values less than 0 are treated as missing. The default output includes the
improvement in Young’s S-stress for successive iterations, two measures of fit for each input
matrix (Kruskal’s stress and the squared correlation, RSQ), and the derived configurations
for each of the dimensions.
Subcommand Order
Subcommands can be named in any order.