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＜電子ブック＞
Applied Multidimensional Scaling and Unfolding / by Ingwer Borg, Patrick J.F. Groenen, Patrick Mair
(SpringerBriefs in Statistics. ISSN:21915458)

版	2nd ed. 2018.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2018
大きさ	IX, 122 p. 65 illus : online resource
著者標目	*Borg, Ingwer author Groenen, Patrick J.F author Mair, Patrick author SpringerLink (Online service)
件　名	LCSH:Mathematical statistics—Data processing LCSH:Psychometrics LCSH:Social sciences—Statistical methods LCSH:Biometry LCSH:Information visualization LCSH:Sociology—Methodology FREE:Statistics and Computing FREE:Psychometrics FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Biostatistics FREE:Data and Information Visualization FREE:Sociological Methods
一般注記	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 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) HTTP:URL=https://doi.org/10.1007/978-3-319-73471-2

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