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An Introduction to Applied Multivariate Analysis
Contributor(s): Raykov, Tenko (Author), Marcoulides, George A. (Author)
ISBN: 0805863753     ISBN-13: 9780805863758
Publisher: Routledge
OUR PRICE:   $161.50  
Product Type: Hardcover - Other Formats
Published: March 2008
Qty:
Additional Information
BISAC Categories:
- Business & Economics | Statistics
- Mathematics | Probability & Statistics - Multivariate Analysis
Dewey: 519.535
LCCN: 2007039834
Physical Information: 1.2" H x 6.1" W x 9" (1.80 lbs) 496 pages
 
Descriptions, Reviews, Etc.
Publisher Description:

This comprehensive text introduces readers to the most commonly used multivariate techniques at an introductory, non-technical level. By focusing on the fundamentals, readers are better prepared for more advanced applied pursuits, particularly on topics that are most critical to the behavioral, social, and educational sciences. Analogies between the already familiar univariate statistics and multivariate statistics are emphasized throughout. The authors examine in detail how each multivariate technique can be implemented using SPSS and SAS and Mplus in the book's later chapters. Important assumptions are discussed along the way along with tips for how to deal with pitfalls the reader may encounter. Mathematical formulas are used only in their definitional meaning rather than as elements of formal proofs.

A book specific website - www.psypress.com/applied-multivariate-analysis - provides files with all of the data used in the text so readers can replicate the results. The Appendix explains the data files and its variables. The software code (for SAS and Mplus) and the menu option selections for SPSS are also discussed in the book. The book is distinguished by its use of latent variable modeling to address multivariate questions specific to behavioral and social scientists including missing data analysis and longitudinal data modeling.

Ideal for graduate and advanced undergraduate students in the behavioral, social, and educational sciences, this book will also appeal to researchers in these disciplines who have limited familiarity with multivariate statistics. Recommended prerequisites include an introductory statistics course with exposure to regression analysis and some familiarity with SPSS and SAS.