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Cited by 429

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Elements of Dual Scaling: An Introduction to Practical Data Analysis.

Meulman, Jacqueline J., Nishisato, Shizuhiko·1995·Journal of the American Statistical Association

Abstract

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.

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Metadata

Authors
Meulman, J. J., & Nishisato, S.
Venue
Journal of the American Statistical Association
Publisher
JSTOR
Published
1995-03
Volume
90
Issue
429
Pages
395
ISSN
0162-1459 (ISSN-L)
OpenAlex
W2014944622
Cited by
429
References
0
FWCI
10.41
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Elements of Dual Scaling: An Introduction to Practical Data Analysis. https://app.cochat.ai/papers/10.2307/2291181
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Meulman, J. J., & Nishisato, S. (1995). Elements of Dual Scaling: An Introduction to Practical Data Analysis. Journal of the American Statistical Association. https://doi.org/10.2307/2291181

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