Multidimensional Scaling Introductory Overview - MDS and Factor Analysis
Even though there are similarities in the type of research questions to which these two procedures can be applied, Multidimensional Scaling and Factor Analysis are fundamentally different methods. Factor analysis requires that the underlying data are distributed as multivariate normal, and that the relationships are linear. MDS imposes no such restrictions. As long as the rank-ordering of distances (or similarities) in the matrix is meaningful, MDS can be used. In terms of resultant differences, factor analysis tends to extract more factors (dimensions) than MDS; as a result, MDS often yields more readily, interpretable solutions. Most importantly, however, MDS can be applied to any kind of distances or similarities, while factor analysis requires us to first compute a correlation matrix. MDS can be based on subjects' direct assessment of similarities between stimuli, while factor analysis requires subjects to rate those stimuli on some list of attributes (for which the factor analysis is performed).
In summary, MDS methods are applicable to a wide variety of research designs because distance measures can be obtained in any number of ways (for different examples, refer to the References topic).