Let's say I have a year's worth of magazine issues (January, February, March, etc), and I want to visualize the differences among them. The classic example of multidimensional scaling (MDS) would have a triangular matrix of subjective difference scores between each possible pair of magazine issues. MDS is then applied to these difference scores.
However, what if instead of simple difference scores, I have a collection of objective sub-features for each issue to use for differentiation. For example:
Number of pages
Number of advertisements
Mean words / page
and so on...
I ultimately wish to acquire some sort of distance between each magazine pair while accounting for each of these features.
How can I use MDS while also taking into account these features? Or is there an alternative technique that is more appropriate?