# Multidimensional Scaling

The use of Multidimensional Scaling is most appropriate when the goal of your analysis is to find the structure in a set of distance measures between a single set of objects or cases. This is accomplished by assigning observations to specific locations in a conceptual low-dimensional space so that the distances between points in the space match the given (dis)similarities as closely as possible. The result is a least-squares representation of the objects in that low-dimensional space, which, in many cases, will help you further understand your data.

**Relation to other
Categories procedures. **When you have multivariate data
from which you create distances and which you then analyze with multidimensional
scaling, the results are similar to analyzing the data using categorical
principal components analysis with object principal normalization.
This kind of PCA is also known as principal coordinates analysis.

**Relation to standard
techniques. **The Categories Multidimensional Scaling procedure
(PROXSCAL) offers several improvements upon the scaling procedure
available in the Statistics Base option (ALSCAL). PROXSCAL offers
an accelerated algorithm for certain models and allows you to put
restrictions on the common space. Moreover, PROXSCAL attempts to minimize
normalized raw stress rather than S-stress (also referred to as **strain**). The normalized raw stress
is generally preferred because it is a measure based on the distances,
while the S-stress is based on the squared distances.