Reading and Understanding Multivariate Statistics.
Grimm, Laurence G. (editor); Yarnold, Paul R. (editor).
Language: English
Published by American Psychological Association (APA), Washington, DC, 1995
- Softcover
- Used
Seller: BOOKHOME SYDNEY, Annandale Sydney, NSW, AustraliaBOOKHOME SYDNEY
AbeBooks seller since January 14, 2000
Condition: Used
£ 16.34
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Add to basketItem description from seller
Paperback small quarto, very good condition, figures, tables, bottom edge little rubbed, pages faintly toned, minor edgewear. 373 pp. This book, edited by Laurence G. Grim and Paul R. Yarnold, is the first book in a series of 2 that presents fundamental conceptual aspects of typical multivariate techniques and measurement topics, and explains these procedures in simple, intuitive terms, using as few equations as possible. The author of each chapter provides example applications of each statistical analysis, the underlying assumptions and mechanics of the analysis, and a discussion of an interesting working example. Each chapter includes a glossary of terms and symbols that provides a quick way to access succinct definitions of fundamental concepts discussed in the text. Chapters include multiple regression, path analysis factor analysis, multidimensional scaling, MANOVA, discriminant analysis and understanding meta-analysis. For graduate students, this book may be a useful companion to the many standard texts that teach the students to perform multivariate analyses in statistics courses as preparation for doing their own research.…
Seller Inventory # 18606
- Title
- Reading and Understanding Multivariate Statistics.
- Author
- Grimm, Laurence G. (editor); Yarnold, Paul R. (editor).
- Publisher
- American Psychological Association (APA), Washington, DC
- Publication year
- 1995
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1557982732
- ISBN 13
- 9781557982735
- Edition
- Reprint ed.
- Seller catalogs
- RESEARCH & STATISTICS
The book presents an overview of multivariate statistics and their place in research. It describes the appropriate context for-and the types of empirical questions that can best be addressed by-each technique or family of techniques, as well as the distribution assumptions that must be met for the analysis to be meaningful.
The most commonly used multivariate techniques are examined in detail: multiple regression and correlation path analysis principal-components analysis exploratory and confirmatory factor analysis multidimensional scaling analysis of cross-classified data logistic regression multivariate analysis of variance (MANOVA) discriminant analysis meta-analysis.
"Synopsis" may belong to another edition of this title.
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