Multidimensional scaling (MDS) allows a researcher to measure an item in more than one dimension at a time. The basic assumption is that people perceive a set of objects as being more or less similar to one another on a number of dimensions (usually uncorrelated with one another) instead of only one.
There are several MDS techniques (also known as techniques for dimensional reduction) often used for the purpose of revealing patterns of one sort or another in interdependent data structures. If data happen to be non-metric, MDS involves rank ordering each pair of objects in terms of similarity.
Then the judged similarities are transformed into distances through statistical manipulations and are consequently shown in n-dimensional space in a way that the interpoint distances best preserve the original interpoint proximities. After this sort of mapping is performed, the dimensions are usually interpreted and labeled by the researcher.
The significance of MDS lies in the fact that it enables the researcher to study ―The perceptual structure of a set of stimuli and the cognitive processes underlying the development of this structure. MDS provides a mechanism for determining the truly salient attributes without forcing the judge to appear irrational.‖ With MDS, one can scale objects, individuals or both with a minimum of
information. The MDS analysis will reveal the most salient attributes which happen to be the primary determinants for making a specific decision.
