Detection of underlying dimensions that explain observed dissimilarities between the objects

Multidimensional Scaling

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 (distances) between the investigated objects. In factor analysis, the similarities between objects (e.g., variables) are expressed in the correlation matrix. With MDS one may analyze any kind of similarity or dissimilarity matrix, in addition to correlation matrices.

The objectives of MDS are:

- identification of dimensions (criteria), which are the basis for perception of objects
- identification of the position of the objects
- interpretation of the dimensions

Output of the analysis is plot called MDS map. It allows you to see the dissimilarities of objects on a two-dimensional scatter plot.

Applications

MDS methods used to be very popular in psychological research on person perception where similarities between trait descriptors were analyzed to uncover the underlying dimensionality of people's perceptions of traits (Rosenberg, 1977). They are also very popular in marketing research, in order to detect the number and nature of dimensions underlying the perceptions of different brands or products (Carmone, 1970).