Elements of dual scaling : an introduction to practical data analysis

著者

書誌事項

Elements of dual scaling : an introduction to practical data analysis

Shizuhiko Nishisato

L. Erlbaum Associates, 1994

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注記

Includes bibliographical references (p. 357-371) and indexes

内容説明・目次

内容説明

Quantification methodology of categorical data is a popular topic in many branches of science. Most books, however, are either too advanced for those who need it, or too elementary to gain insight into its potential. This book fills the gap between these extremes, and provides specialists with an easy and comprehensive reference, and others with a complete treatment of dual scaling methodology -- starting with motivating examples, followed by an introductory discussion of necessary quantitative skills, and ending with different perpsectives on dual scaling with examples, advanced topics, and future possibilities. This book attempts to successively upgrade readers' readiness for handling analysis of qualitative, categorical, and non-metric data, without overloading them. The writing style is very friendly, and difficult topics are always accompanied by simple illlustrative examples. There are a number of topics on dual scaling which were previously addressed only in journal articles or in publications that are not readily available. Integration of these topics into the standard framework makes the current book unique, and its extensive coverage of relevant topics is unprecedented. This book will serve as both reference and textbook for all those who want to analyze categorical data effectively.

目次

Contents: Preface. Part I: Background. To Begin With. What Can Dual Scaling Do for You? Is Your Data Set Appropriate for Dual Scaling? Some Fundamentals for Dual Scaling. Useful Quantitative Tools. Mathematics of Dual Scaling. Part II: Incidence Data. Contingency/Frequency Tables. Multiple-Choice Data. Sorting Data. Part III: Dominance Data. Paired Comparison Data. Rank-Order Data. Successive Categories (Rating) Data. Part IV: Special Topics. Forced Classification and Focused Analysis. Graphical Display. Outliers and Missing Responses in Multiple-Choice Data. Analysis of Multiway Data. Additional Topics and Future Possibilities.

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