A statistical technique for displaying differences between items, as if they were points on a map, or in a 3-dimensional space. The greater the distance, the more different the items are - in the opinions of people who rated them. This is the statistical technique used for perceptual mapping.

A technique that takes a matrix of dissimilarities or 'distances' (metric and non-metric) among a set of objects and creates a spatial configuration of these objects such that the distances between pairs of objects match as closely as possible the dissimilarities. In using the technique, one strives to have as few dimensions as possible in the configuration; two are the most ideal for mapping the pattern.

A statistical technique that allows attitudinal data to be collected for several attributes in a manner that allows data analysis to produce a single overall rating of a retailer (rather than a profile of individual characteristics).

This can be thought of as an alternative to factor analysis. In a similar way it aims to uncover underlying dimensions in the data, but a variety of measures of distance can be used. A common example is to take a matrix of distances between cities (such as that found at the front of a road atlas). Using MDS an analysis in two dimensions would produce something very similar to a map.

In multidimensional scaling, the objective is to transform consumer judgments of similarity or preference (e.g., preference for stores or brands) into distances represented in multidimensional space. If objects A and B are judged by respondents as being the most similar compared with all other possible pairs of objects, multidimensional scaling techniques will position objects A and B in such a way that the distance between them in the multidimensional space is smaller than the distance between any other two pairs of objects. The resulting perceptual maps show the relative positioning of all objects, but additional analysis is needed to assess which attributes predict the position of each object (Hair et al., 1995).

Multidimensional scaling (MDS) is a set of related statistical techniques often used in data visualisation for exploring similarities or dissimilarities in data. An MDS algorithm starts with a matrix of item-item similarities, then assigns a location of each item in a low-dimensional space, suitable for graphing or 3 D visualisation.