Abstract
The different purposes of MDS are explained: MDS as a psychological model of similarity judgments; MDS for visualizing proximity data; and MDS for testing structural hypotheses.
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Notes
- 1.
Most early MDS programs were set, by default, to deliver a 2-dimensional solution for data that were assumed to have an ordinal scale level.
- 2.
This is easy to see from an example: If point \(i\) has the coordinates \((0,0)\) and \(j\) the coordinates \((3,2)\), we get the intra-dimensional distances \(|0-3|=3\) and \(|0-2|=2\), respectively. The overall distance \(d_{ij}\), with \(p=1\), is thus equal to \(2+3=5.00\). For \(p=2\), the overall distance is 3.61. For \(p=10\), it is equal to 3.01.
- 3.
For the consequences of choosing other coordinate systems and for the many peculiar laws of such “taxicab geometries”, see http://taxicabgeometry.net.
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Borg, I., Groenen, P.J.F., Mair, P. (2013). The Purpose of MDS. In: Applied Multidimensional Scaling. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31848-1_2
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