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Applied Multidimensional Scaling.

Author: Ingwer Borg; Patrick J F Groenen; Patrick Mair
Publisher: Cham : Springer, 2018.
Series: SpringerBriefs in Statistics Ser.
Edition/Format:   eBook : Document : English : 2nd edView all editions and formats
Summary:
"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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Borg, Ingwer.
Applied Multidimensional Scaling.
Cham : Springer, ©2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Ingwer Borg; Patrick J F Groenen; Patrick Mair
ISBN: 9783319734712 3319734717
OCLC Number: 1058999427
Description: 1 online resource (128 pages).
Contents: Intro; Preface; Contents; 1 First Steps; 1.1 Basic Ideas of Multidimensional Scaling; 1.2 Basic Ideas of Unfolding; 1.3 Summary; References; 2 The Purpose of MDS and Unfolding; 2.1 MDS for Visualizing Proximity Data; 2.2 MDS for Uncovering Latent Dimensions of Judgment; 2.3 Distance Formulas as Models of Judgment; 2.4 MDS for Testing Structural Hypotheses; 2.5 Unfolding as a Psychological Model of Preference; 2.6 Summary; References; 3 The Fit of MDS and Unfolding Solutions; 3.1 The Global Stress of MDS Solutions; 3.2 Evaluating Stress Statistically; 3.3 Stress and MDS Dimensionality. 3.4 Stress Per Point3.5 Conditions Causing High Stress in MDS; 3.6 Stress in Unfolding; 3.7 Stability of MDS Solutions; 3.8 Summary; References; 4 Proximities; 4.1 Direct Proximities; 4.2 Derived Proximities; 4.3 Proximities from Index Conversions; 4.4 Co-occurrence Data; 4.5 The Gravity Model for Co-occurrences; 4.6 Summary; References; 5 Variants of MDS Models; 5.1 The Type of Regression in MDS; 5.2 Euclidean and Other Distances; 5.3 MDS of Asymmetric Proximities; 5.4 Modeling Individual Differences in MDS; 5.5 Scaling Replicated Proximities; 5.6 Weighting Proximities in MDS; 5.7 Summary. 7.8 Always Interpreting Principal Axes Dimensions7.9 Always Interpreting Dimensions or Directions; 7.10 Poorly Dealing with Disturbing Points; 7.11 Scaling Almost-Equal Proximities; 7.12 Summary; References; 8 Unfolding; 8.1 Unfolding in Three-Dimensional Space; 8.2 Multidimensional Versus Multiple Unfolding; 8.3 Conditionalities in Unfolding; 8.4 Stability of Unfolding Solutions; 8.5 Degenerate Unfolding Solutions; 8.6 Special Unfolding Models; 8.7 Summary; References; 9 MDS Algorithms; 9.1 Classical MDS; 9.2 Iterative MDS Algorithms; 9.3 Summary; References; 10 MDS Software; 10.1 Proxscal. 10.2 The R Package smacof10.2.1 Functions in smacof; 10.2.2 A Simple MDS Example; References; Index.
Series Title: SpringerBriefs in Statistics Ser.

Abstract:

This book introduces multidimensional scaling (MDS) and unfolding as data analysis techniques for applied researchers.  Read more...

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"'This book introduces the multidimensional scaling (MDS) as a psychological model and as a data analysis technique for the applied researcher. ... The book is unique in its orientation on the Read more...

 
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