Multidimensional Similarity Structure Analysis

Multidimensional Similarity Structure Analysis
Author: I. Borg
Publisher: Springer Science & Business Media
Total Pages: 402
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461247683

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.

Modern Multidimensional Scaling

Modern Multidimensional Scaling
Author: Ingwer Borg
Publisher: Springer Science & Business Media
Total Pages: 469
Release: 2013-04-18
Genre: Mathematics
ISBN: 1475727119

Multidimensional scaling (MDS) is a technique for the analysis of similarity or dissimilarity data on a set of objects. Such data may be intercorrelations of test items, ratings of similarity on political candidates, or trade indices for a set of countries. MDS attempts to model such data as distances among points in a geometric space. The main reason for doing this is that one wants a graphical display of the structure of the data, one that is much easier to understand than an array of numbers and, moreover, one that displays the essential information in the data, smoothing out noise. There are numerous varieties of MDS. Some facets for distinguishing among them are the particular type of geometry into which one wants to map the data, the mapping function, the algorithms used to find an optimal data representation, the treatment of statistical error in the models, or the possibility to represent not just one but several similarity matrices at the same time. Other facets relate to the different purposes for which MDS has been used, to various ways of looking at or "interpreting" an MDS representation, or to differences in the data required for the particular models. In this book, we give a fairly comprehensive presentation of MDS. For the reader with applied interests only, the first six chapters of Part I should be sufficient. They explain the basic notions of ordinary MDS, with an emphasis on how MDS can be helpful in answering substantive questions.

Multidimensional Scaling

Multidimensional Scaling
Author: Joseph B. Kruskal
Publisher: SAGE Publications
Total Pages: 100
Release: 1978-01-01
Genre: Social Science
ISBN: 1506320880

Outlines a set of techniques that enables a researcher to explore the hidden structure of large databases. These techniques use proximities to find a configuration of points that reflect the structure in the data.

Tensor Analysis and Multidimensional Scaling

Tensor Analysis and Multidimensional Scaling
Author: Richard M. Fenker
Publisher:
Total Pages: 31
Release: 1972
Genre: Calculus of tensors
ISBN:

E ASSUMPTION IS VIOLATED IN CASES WHERE THE SAMPLE OF STIMULUS OBJECTS HAS AN UNDERLYING CLASS STRUCTURE. It shows that while the prediction of similarity judgments for pairs of stimuli belonging to the same class is consistent with the scaling model, the prediction of between class judgments does not fit the model. In particular, between class similarity depends on the separation between the two stimuli being compared and the centroids or prototypes of the classes as well as on the distance between the stimuli in the multidimensional space. That is, the perceived distance (dissimilarity) between two stimuli depends not only on coordinate differences but on the relative position of the pair in the geometric space. This result is inconsistent with the assumptions underlying the multidimensional scaling model. A pseudo proof is constructed to demonstrate this inconsistency. (Author).

Cross-Cultural Analysis

Cross-Cultural Analysis
Author: Eldad Davidov
Publisher: Routledge
Total Pages: 551
Release: 2018-01-31
Genre: Psychology
ISBN: 1134991290

Intended to bridge the gap between the latest methodological developments and cross-cultural research, this interdisciplinary resource presents the latest strategies for analyzing cross-cultural data. Techniques are demonstrated through the use of applications that employ cross-national data sets such as the latest European Social Survey. With an emphasis on the generalized latent variable approach, internationally prominent researchers from a variety of fields explain how the methods work, how to apply them, and how they relate to other methods presented in the book. Syntax and graphical and verbal explanations of the techniques are included. Online resources, available at www.routledge.com/9781138690271, include some of the data sets and syntax commands used in the book. Applications from the behavioral and social sciences that use real data-sets demonstrate: The use of samples from 17 countries to validate the resistance to change scale across these nations How to test the cross-national invariance properties of social trust The interplay between social structure, religiosity, values, and social attitudes A comparison of anti-immigrant attitudes and patterns of religious orientations across European countries. The second edition includes six new chapters and two revised ones presenting exciting developments in the literature of cross-cultural analysis including topics such as approximate measurement invariance, alignment optimization, sensitivity analyses, a mixed-methods approach to test for measurement invariance, and a multilevel structural equation modeling approach to explain noninvariance. This book is intended for researchers, practitioners, and advanced students interested in cross-cultural research. Because the applications span a variety of disciplines, the book will appeal to researchers and students in: psychology, political science, sociology, education, marketing and economics, geography, criminology, psychometrics, epidemiology, and public health, as well as those interested in methodology. It is also appropriate for an advanced methods course in cross-cultural analysis.

Color Image Processing and Applications

Color Image Processing and Applications
Author: Konstantinos N. Plataniotis
Publisher: Springer Science & Business Media
Total Pages: 368
Release: 2013-04-17
Genre: Computers
ISBN: 3662041863

Reporting the state of the art of colour image processing, this monograph fills a gap in the literature on digital signal and image processing. It contains numerous examples and pictures of colour image processing results, plus a library of algorithms implemented in C.

Applied Multidimensional Scaling and Unfolding

Applied Multidimensional Scaling and Unfolding
Author: Ingwer Borg
Publisher: Springer
Total Pages: 128
Release: 2018-05-16
Genre: Computers
ISBN: 3319734717

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).