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Social forskning og statistik

Elements of Dual Scaling

An Introduction To Practical Data Analysis

Udgivet af Taylor & Francis Ltd

Bogdetaljer

  • Hæftet
  • 398 sider
  • Engelsk
ISBN 9781138876323
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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…

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Bogdetaljer
Forlag Taylor & Francis Ltd
Kategori Social forskning og statistik
ISBN-13 9781138876323
EAN 9781138876323
Format Hæftet
Sprog Engelsk
sider 398
Undertitel - An Introduction To Practical Data Analysis
Produktbeskrivelse
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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