Applied Multidimensional Scaling and Unfolding (SpringerBriefs in Statistics)
Ingwer Borg
- 出版商: Springer
- 出版日期: 2018-05-25
- 售價: $2,900
- 貴賓價: 9.5 折 $2,755
- 語言: 英文
- 頁數: 132
- 裝訂: Paperback
- ISBN: 3319734709
- ISBN-13: 9783319734705
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相關分類:
機率統計學 Probability-and-statistics
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商品描述
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).
商品描述(中文翻譯)
本書介紹了多維尺度分析(MDS)和展開作為應用研究者的數據分析技術。MDS 用於分析一組物件的接近度數據,將數據表示為幾何空間中點之間的距離(通常是二維的)。展開是一種相關的方法,將偏好數據(通常是不同人對一組物件的評價)映射為兩組點之間的距離(分別代表人和物件)。
本書第二版已全面修訂,以反映新發展,並且對展開的涵蓋範圍也有了大幅擴展。該書旨在幫助應用研究者,特別是那些主要關注將這些方法作為建立實質理論工具的研究者,討論了眾多應用(包括經典和近期的),突出了實際問題(如評估模型擬合),提出了強化理論預期的尺度解決方案的方法,並針對 MDS/展開使用者常見的錯誤進行了說明。此外,本書展示了如何在實際研究工作中使用 MDS 和展開,主要通過在 R 環境中使用 smacof 套件,但也包括在 SPSS 中使用 Proxscal。這是一本對心理學家、社會科學家和市場研究者非常有價值的資源,前提是他們對多變量統計(如多重回歸和因素分析)有基本的理解。