Reading and Understanding Multivariate Statistics.

Grimm, Laurence G. (editor); Yarnold, Paul R. (editor).

ISBN 10: 1557982732 / ISBN 13: 9781557982735

Published by American Psychological Association (APA), Washington, DC, 1995

About this Item

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

Bibliographic Details

• Title: Reading and Understanding Multivariate Statistics.
• Publisher: American Psychological Association (APA), Washington, DC
• Publication Date: 1995
• Language: English
• ISBN 10: 1557982732
• ISBN 13: 9781557982735
• Binding: Soft cover
• Edition: Reprint ed.
• Seller catalogs:
  • RESEARCH & STATISTICS

About this title

Synopsis

Reading and Understanding Multivariate Statistics helps researchers, students, and other readers of research to understand the purpose and presentation of multivariate techniques. The editors focus on providing a conceptual understanding of the meaning of the statistics in the context of the research questions and results; they leave the subject of how to perform multivariate analysis to other texts.

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.