Metric scaling : correspondence analysis
著者
書誌事項
Metric scaling : correspondence analysis
（Sage university papers series, . Quantitative applications in the social sciences ; no. 07-075）
Sage Publications, c1990
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07.75
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注記
Bibliography: p. 92-95
内容説明・目次
内容説明
Presents a set of closely related techniques that facilitate the exploration and display of a wide variety of multivariate data, both categorical and continuous. Three methods of metric scaling, correspondence analysis, principal components analysis, and multiple dimensional preference scaling are explored in detail for strengths and weaknesses over a wide range of data types and research situations. "The introduction illustrates the methods with a small dataset. This approach is effective--in a few minutes, with no mathematical requirement, the reader can understand the capabilities, similarities, and differences of the methods. . . . Numerical examples facilitate learning. The authors use several examples with small datasets that illustrate very well the links and the differences between the methods. . . . we find this text very good and recommend it for graduate students and social science researchers, especially those who are interested in applying some of these methods and in knowing the relationship among them." --Journal of Marketing Research "Illustrate[s] the service Sage provides by making high-quality works on research methods available at modest prices. . . . The authors use several interesting examples of practical applications on data sets, ranging from contraception preferences, to pottery shards from archeological digs, to durable consumer goods from market research. These examples indicate the broad range of possible applications of the method to social science data." --Contemporary Sociology "The book is a bargain; it is clearly written." --Journal of Classification
目次
Introduction
The Basic Structure of a Data Matrix
Principal Components Analysis
Multidimensional Preference Scaling
Correspondence Analysis of Contingency Tables
Correspondence Analysis of Non-Frequency Data
Ordination, Seriation, and Guttman Scaling
Multiple Correspondence Analysis
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