| 期刊全稱 | Applied Multidimensional Scaling and Unfolding | | 影響因子2023 | Ingwer Borg,Patrick J.F. Groenen,Patrick Mair | | 視頻video | http://file.papertrans.cn/160/159971/159971.mp4 | | 發(fā)行地址 | Provides a concise, largely conceptual introduction to multidimensional scaling and unfolding.Focuses on how to actually run and interpret MDS and unfolding in applied research (with examples from psy | | 學(xué)科分類 | SpringerBriefs in Statistics | | 圖書封面 |  | | 影響因子 | .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 Proxsc | | Pindex | Book 2018Latest edition |
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