Applied Multidimensional Scaling and Unfolding

By: Ingwer Borg, Patrick J.F. Groenen, Patrick Mair

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Paperback | 25 May 2018 | Edition Number 2

At a Glance

Paperback
134 Pages

Edition Type
Revised

Dimensions(cm)
22.86 x 15.24 x 0.64

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This book introduces multidimensional scaling (MDS) and unfolding as data analysis techniques for applied researchers. MDS is used for the analysis of proximity data on a set of objects, representing the data as distances between points in a geometric space (usually of two dimensions). Unfolding is a related method that maps preference data (typically evaluative ratings of different persons on a set of objects) as distances between two sets of points (representing the persons and the objects, resp.).

This second edition has been completely revised to reflect new developments and the coverage of unfolding has also been substantially expanded. Intended for applied researchers whose main interests are in using these methods as tools for building substantive theories, it discusses numerous applications (classical and recent), highlights practical issues (such as evaluating model fit), presents ways to enforce theoretical expectations for the scaling solutions, and addresses the typical mistakes that MDS/unfolding users tend to make. Further, it shows how MDS and unfolding can be used in practical research work, primarily by using the smacof package in the R environment but also Proxscal in SPSS. It is a valuable resource for psychologists, social scientists, and market researchers, with a basic understanding of multivariate statistics (such as multiple regression and factor analysis).

Industry Reviews

"'This book introduces the multidimensional scaling (MDS) as a psychological model and as a data analysis technique for the applied researcher. ... 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. ... 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.'" (Ludwig Paditz, zbMATH 1409.62006, 2019)

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