An Introduction to Applied Multivariate Analysis

This comprehensivetext 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.

Preface. 1. Introduction to Multivariate Statistics. 2. Elements of Matrix Theory. 3. Data Screening and Preliminary Analyses. 4. Multivariate Analysis of Group Differences. 5. Repeated Measure Analysis of Variance. 6. Analysis of Covariance. 7. Principal Component Analysis. 8. Exploratory Factor Analysis. 9. Confirmatory Factor Analysis. 10. Discriminant Function Analysis. 11. Canonical Correlation Analysis. 12. An Introduction to the Analysis of Missing Data. 13. Multivariate Analyses of Change Processes. References. Appendix.

Tenko Raykov is a Professor of Measurement and Quantitative Methods at Michigan State University. He received his Ph.D. in Mathematical Psychology from Humboldt University in Berlin. He is an editorial board member of the British Journal of Mathematical and Statistical Psychology, Multivariate Behavioral Research, Psychological Methods, and Structural Equation Modeling. George A. Marcoulides is a Professor of Research Methods and Statistics at the University of California, Riverside. He is the Editor of Structural Equation Modeling, the Quantitative Methodology Series, and an editorial board member of numerous other measurement and statistics journals.