UID:
almahu_9947362985602882
Format:
XIV, 390 p.
,
online resource.
ISBN:
9781461247685
Content:
Multidimensional Similarity Structure Analysis comprises a class of models that represent similarity among entities (for example, variables, items, objects, persons, etc.) in multidimensional space to permit one to grasp more easily the interrelations and patterns present in the data. The book is oriented to both researchers who have little or no previous exposure to data scaling and have no more than a high school background in mathematics and to investigators who would like to extend their analyses in the direction of hypothesis and theory testing or to more intimately understand these analytic procedures. The book is repleted with examples and illustrations of the various techniques drawn largely, but not restrictively, from the social sciences, with a heavy emphasis on the concrete, geometric or spatial aspect of the data representations.
Note:
1 - Construction of SSA Representations -- 2 - Ordinal SSA by Iterative Optimization -- 3 - Monotone Regression -- 4 - SSA Models, Measures of Fit, and Their Optimization -- 5 - Three Applications of SSA -- 6 - SSA and Facet Theory -- 7 - Degenerate Solutions in Ordinal SSA -- 8 - Computer Simulation Studies on SSA -- 9 - Multidimensional Unfolding -- 10 - Generalized and Metric Unfolding -- 11 - Generalized SSA Procedures -- 12 - Confirmatory SSA (1) -- 13 - Confirmatory SSA (2) -- 14 - Physical and Psychological Spaces -- 15 - SSA as Multidimensional Scaling -- 16 - Scalar Products -- 17 - Matrix Algebra for SSA -- 18 - Mappings of Data in Distances -- 19 - Procrustes Procedures -- 20 - Individual Differences Models -- References -- Name Index.
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9781461291473
Language:
English
DOI:
10.1007/978-1-4612-4768-5
URL:
http://dx.doi.org/10.1007/978-1-4612-4768-5
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