" Each candy bar's average rating across judges is used as a point in a scatterplot with nine points. The scatterplot looks like the constellation Orion. It also looks like a pattern described by two dimensions, sweet and salty. The stress statistic for two dimensions has an acceptable value, but not as good as when a third dimension is added. The third dimension appears to one of texture: creamy vs. crunchy. As Borgatti (1997) observed, there are two things to look for in interpreting an MDS map: clusters and dimensions. An inspection of the clusters of points reveals that highest average rating seems distant from the other eight and falls in the region describable as somewhat sweet, not very salty, and partly crunchy (Snickers). The lowest rating, which has several near neighbors, falls in the very sweet, very salty, and fairly creamy region (Reese). Based on these results,...
He realizes that a good competitor will have to have a name as appealing as Snickers, so he commissions MRO for another job -- helping him decide on a name for the new candy bar.
Business Drivers Multidimensional scaling and factor analysis are a couple of different statistical techniques that can be used to understand the drivers of a business. MDS is a technique that renders multiple data points in a relational manner -- data points that end up closer to one another are expressed visually as such, while those that are fairly far from each other in the key variable are expressed as being far.
Multivariate Techniques There is a number of mathematical and statistical tools that businesses use to survive and thrive in their respective markets. Some of the math involved is quite simple and basic. Examples of such basic operations would include percentages, standard deviations and so forth. However, there are some fields and realms where much more intricate mathematics are involved and statistics would be a common example of such a complex method.
WidgeCorp III There a number of techniques for performing multivariate analysis. These include factor analysis, multidimensional scaling and cluster analysis. The first of these, factor analysis, is used to determine "something about the nature of the independent variables that affect the dependent variables," without actually measuring the independent variables (Darlington, n.d.). DeCoster (1998) notes that there are two types of factor analysis: exploratory factor analysis and confirmatory factor analysis. As DeCoster points
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