Multidimensional scaling operates on a symmetric matrix whose values may be interpreted, in a general sense,
as distances between a set of objects. The objective is to find a set of coordinates whose inter-point distances
match, as closely as possible, those of the input data matrix. When plotted, the coordinates provide a display
which can be interpreted in the same way as a map: for example, if points in the display are close together, their
distance apart in the data matrix was small.
Distance Matrix
Specifies the symmetric matrix of inter-point distances.
Available Data
This lists symmetric matrices that can be used to specify the distance data. Double-click on a name to copy it to
the current input field; alternatively, you can type the name directly into the input field.
Method
Controls whether metric or non-metric scaling is given. The algorithm involves regression of the distances, calculated
from the solution coordinates, against the distances in the data matrix. Non-metric scaling uses monotonic
regression, whereas metric scaling uses linear regression through the origin.
Number of Dimensions
Sets the number of dimensions required for the solution. Entering a list of numbers carries out a series of scaling
operations, all based on the same matrix of dissimilarities, but with different numbers of dimensions.
Options
You can control various aspects of the algorithm used for the analysis from the
Options menu, and also select which results are to be printed.
See Also