# Multidimensional scaling

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The technique is also used in marketing, see Multidimensional scaling in marketing

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 3D visualisation.

## Categorization of MDS

MDS algorithms fall into a taxonomy, depending on the meaning of the input matrix:

## Applications

Applications include scientific visualisation and data mining in fields such as cognitive science, psychophysics, psychometrics and ecology.

### Marketing

In marketing, MDS is a statistical technique for taking the preferences and perceptions of respondents and representing them on a visual grid. These grids, called perceptual maps are usually two-dimensional, but they can represent more than two.

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’ judgements 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’ judgements about pairs of products. Because of these advantages, MDS is the most common technique used in perceptual mapping.

#### Multidimensional scaling procedure

There are several steps in conducting MDS research:

1. Formulating the problem - What brands do you want to compare? How many brands do you want to compare? More than 20 is cumbersome. Less than 8 (4 pairs) will not give valid results. What purpose is the study to be used for?
2. Obtaining Input Data - Respondents are asked a series of questions. For each product pair they are asked to rate similarity (usually on a 7 point Likert scale from very similar to very dissimilar). The first question could be for Coke/Pepsi for example, the next for Coke/Hires rootbeer, the next for Pepsi/Dr Pepper, the next for Dr Pepper/Hires rootbeer, etc. The number of questions is a function of the number of brands and can be calculated as Q = N (N - 1) / 2 where Q is the number of questions and N is the number of brands. This approach is referred to as the “Perception data : direct approach”. There are two other approaches. There is the “Perception data : derived approach” in which products are decomposed into attributes which are rated on a semantic differential scale. The other is the “Preference data approach” in which respondents are asked their preference rather than similarity.
3. Running the MDS statistical program - Software for running the procedure is available in most of the better statistical applications programs. Often there is a choice between Metric MDS (which deals with interval or ratio level data), and Nonmetric MDS (which deals with ordinal data). The researchers must decide on the number of dimensions they want the computer to create. The more dimensions, the better the statistical fit, but the more difficult it is to interpret the results.
4. Mapping the results and defining the dimensions - The statistical program (or a related module) will map the results. The map will plot each product (usually in two dimensional space). The proximity of products to each other indicate either how similar they are or how preferred they are, depending on which approach was used. The dimensions must be labelled by the researcher. This requires subjective judgement and is often very challenging. The results must be interpreted ( see perceptual mapping).
5. Test the results for reliability and Validity - Compute R-squared to determine what proportion of variance of the scaled data can be accounted for by the MDS procedure. An R-square of .6 is considered the minimum acceptable level. Other possible tests are Kruskal’s Stress, split data tests, data stability tests (ie.: eliminating one brand), and test-retest reliability.