multidimensional scaling: Using SPSS/PROXSCAL Multidimensional scaling attempts to find the structure in a set of proximity measures between objects. This process is accomplished by assigning observations to specific locations in a conceptual low-dimensional space such that the distances between points in the space match the given (dis)similarities as closely as possible. The result is a least-squares representation of the objects in that low-dimensional space, which, in many cases, will help you to further understand your data.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Teuerle, Marek; Żebrowski, Piotr; Magdziarz, Marcin: Multidimensional Lévy walk and its scaling limits (2012)
- Köhn, Hans-Friedrich: Combinatorial individual differences scaling within the city-block metric (2006)
- de Rooij, Mark: Distance association models for the analysis of repeated transition frequency tables. (2001)