Multidimensional+Scaling

Multidimensional Scaling (Multivariate Methods) (GWU EMSE-271)
Index | Topics (Logical Lectures) | Lectures | Problems | Readings | Nomenclature | Concepts


 * "Multidimensional scaling** (**MDS**) is a set of related statistical techniques often used in information visualization for exploring similarities or dissimilarities in data. MDS is a special case of ordination. An MDS algorithm starts with a matrix of item–item similarities, then assigns a location to each item in //N//-dimensional space, where //N// is specified a priori. For sufficiently small //N//, the resulting locations may be displayed in a graph or 3D visualisation." - [|Wikipedia]

Multidimensional scaling is similar to clustering; the first provides a spatial relationship with interval scale variables; the second categorizations with nominal scale variables. - Lattin


 * Comparison and advantages**

"Potential customers are asked to compare pairs of products and make judgements about their similarity. Whereas other techniques (such as factor analysis, discriminant analysis, and conjoint analysis) obtain underlying dimensions from responses to product attributes identified by the researcher, MDS obtains the underlying dimensions from respondents’ judgments about the similarity of products. This is an important advantage. It does not depend on researchers’ judgments. It does not require a list of attributes to be shown to the respondents. The underlying dimensions come from respondents’ judgments about pairs of products. Because of these advantages, MDS is the most common technique used in perceptual mapping." - [|Wikipedia]


 * Sources:**
 * Multidimensional scaling. (2009, November 23). In //Wikipedia, The Free Encyclopedia//. Retrieved 20:32, December 1, 2009, from []
 * Analyzing Multivariate Data, by James Lattin, Douglas Carroll and Green ([|Amazon]), pp. 12-13