Applied multidimensional scaling and unfolding
著者
書誌事項
Applied multidimensional scaling and unfolding
（Springer Briefs in statistics）
Springer, c2018
2nd ed
- : [pbk.]
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注記
"Extras online"--Cover
Includes bibliographical references and index
内容説明・目次
内容説明
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).
目次
1 First steps.- 2 The purpose of MDS and Unfolding.- 3 The fit of MDS and Unfolding solutions.- 4 Proximities.- 5 Variants of MDS models.- 6 Confirmatory MDS.- 7 Typical mistakes in MDS.- 8 Unfolding.- 9 MDS algorithms.- 10 MDS Software.- Subject Index.
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