1 - 9 of 9 Chapters
[The basic ideas of MDS are introduced doing MDS by hand. Then, MDS is done using a computer program. The goodness of the MDS configuration is evaluated by correlating its distances with the data.]
[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.]
[Ways to assess the goodness of an MDS solution are discussed. The Stress measure and some of its variants are introduced. Criteria for evaluating Stress are presented.]
[The data for MDS, proximities, are discussed. Proximities can be collected directly as judgments of similarity; proximities can be derived from data vectors; proximities may result from converting other indices; and co-occurrence data are yet another popular form of proximities.]
[Various form of MDS are discussed: Ordinal MDS, metric MDS, MDS with different distance functions, MDS for more than one proximity value per distance, MDS for asymmetric proximities, individual differences MDS models, and unfolding.]
[Different forms of confirmatory MDS are introduced, from weak forms with external starting configurations, to enforcing theoretical constraints onto the MDS point coordinates or onto certain regions of the MDS space.]
[Various mistakes that users tend to make when using MDS are discussed, from conceptual fuzziness, over using MDS for the wrong type of data, or using MDS programs with suboptimal specifications, to misinterpreting MDS solutions.]
[Two types of solutions for MDS are discussed. If the proximities are Euclidean distances, classical MDS yields an easy algebraic solution. In most MDS applications, iterative methods are needed, because they admit many types of data and distances. They use a two-phase optimization algorithm,...
[Two modern programs for MDS are described: Proxscal, an Spss module, and Smacof, an R-package. Commands and/or GUI menus are presented and illustrated with practical applications.]
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