Multidimensional Scaling Of N Sets Of Similarity Measures: A Nonmetric Individual Differences Approach

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Abstract
Given N sets of similarity measures between pairs of stimulus objects a nonmetric multidimensional scaling procedure (known as CEMD) is proposed which allows one of four kinds of analysis to be performed on the data. The four types of solution are defined in terms of two binary decisions: (a) whether only one configuration is allowed for all N sets or whether each set is allowed to have its own configuration; and (b) whether the same mono- tonic transformation is applied to all N sets of input data or whether each set is allowed its own monotonic transformation. Where separate configurations are allowed the computer program seeks to keep these solutions as similar as possible and provides an index of the similarity of the configurations.