Applied Multidimensional Scaling and Unfolding. 2nd ed. 2018

種類:

電子ブック

責任表示:

by Ingwer Borg, Patrick J.F. Groenen, Patrick Mair

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2018

著者名:

• Borg, Ingwer.
• Groenen, Patrick J.F.
• Mair, Patrick.
• SpringerLink (Online service)

シリーズ名:

SpringerBriefs in Statistics ;

ISBN:

9783319734712 [3319734717]    

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

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