Multidimensional scaling with regional restrictions for facet theory: an application to Levy's political protest data

Abstract

"Multidimensional scaling (MDS) is often used for the analysis of correlation matrices of items generated by a facet-theory design. The emphasis of the analysis is on regional hypotheses on the location of the items in the MDS solution. An important regional hypothesis is the axial constraint, where the items from different levels of a facet are assumed to be located in different parallel slices. The simplest approach is to do an MDS and draw the parallel lines separating the slices as good as possible by hand. Alternatively, Borg & Shye (1995) proposed to automate the second step. Borg & Groenen (1997, 2005) proposed a simultaneous approach for ordered facets, when the number of MDS dimensions equals the number of facets. In this paper, we propose a new algorithm that estimates an MDS solution subject to axial constraints without the restriction that the number of facets equals the number of dimensions. The algorithm is based on constrained iterative majorization of De Leeuw & Heiser (1980) with special constraints. This algorithm is applied to Levy's (1983) data on political protests." (author's abstract)