Multidimensional scaling (MDS) can be considered to be an alternative to factor analysis. In general, the goal of the analysis is to detect meaningful underlying dimensions that allow the researcher to explain observed similarities or dissimilarities between the investigated objects. In factor analysis, the similarities between objects (e.g. variables) are expressed in the correlation matrix. With MDS one may analyse any kind of similarity or dissimilarity matrix, in addition to correlation matrices.
This outcome is visualised in a 2 dimensional map, which gives the researcher an immediate feel of how differentiating the questions were. Questions which are clustered together did get very similar scores by all respondents. This can be very useful when optimising a questionnaire or to differentiate consumers based on the most distinct questions.
Even though there are similarities in the type of research questions to which MDS and factor analysis can be applied, they are fundamentally different methods. Factor analysis requires that the underlying data is distributed as multivariate normal, and that the relationships are linear. MDS imposes no such restrictions. Just as long as the rank-ordering similarities in the matrix are 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 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.