Applied Multidimensional Scaling
Springer Science & Business Media, Oct 30, 2012 - Computers - 113 pages
This book introduces MDS as a psychological model and as a data analysis technique for the applied researcher. It also discusses, in detail, how to use two MDS programs, Proxscal (a module of SPSS) and Smacof (an R-package). The book is unique in its orientation on the applied researcher, whose primary interest is in using MDS as a tool to build substantive theories. This is done by emphasizing practical issues (such as evaluating model fit), by presenting ways to enforce theoretical expectations on the MDS solution, and by discussing typical mistakes that MDS users tend to make. The primary audience of this book are psychologists, social scientists, and market researchers. No particular background knowledge is required, beyond a basic knowledge of statistics.
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2-dimensional analysis Applied Multidimensional Scaling Auto Theft Borg and Groenen Brazil city-block distances classical MDS cluster co-occurrence data co-occurrences common space confirmatory MDS CONGO constraints coordinates correlations corresponding data matrix default diagonal dij(X dimension weights dimensional weighting dimensionality disparities equal Euclidean distances example exploratory MDS external scales facet grid Hence ideal points IDIOSCAL individual differences interpret interval MDS Keywords Larceny lead loss function MDS configuration MDS models MDS plane MDS program MDS representation MDS solution MDS space meaningful menu metric minimal Minkowski distances number of iterations number of points optimal options ordinal MDS persons plot primary approach Procrustean proximity data proximity matrices Proxscal psychological re-scaling rectangles regions regression replications represent restrictions rotated scale level Shepard diagram similarity ratings Smacof solution in Fig specify SpringerBriefs in Statistics Spss starting configuration Stress value Stress-1 structure substantive Systat Table test items theory transformations unfolding variables vectors Weber–Fechner law ylab