Applied Multivariate Research: Design and Interpretation

Multivariate designs were once the province of the very few exalted researchers who understood the underlying advanced mathematics. Today, through the sophistication of statistical software packages such as SPSS, virtually all graduate students across the social and behavioural sciences are exposed to the complex multivariate statistical techniques without having to learn the mathematical computations needed to acquire the data output. These students - in psychology, education, political science, etc. - will never be statisticians and appropriately so, their preparation and coursework reflects less of an emphasis on the mathematical complexities of multivariate statistics and more on the analysis and the interpretation of the methods themselves and the actual data output. This book provides full coverage of the wide range of multivariate topics in a conceptual, rather than mathematical, approach. The author gears toward the needs, level of sophistication, and interest in multivariate methodology of students in applied areas that need to focus on design and interpretation rather than the intricacies of specific computations. The book includes: - Coverage of the most widely used multivariate designs: multiple regression, exploratory factor analysis, MANOVA, and structural equation modeling. - Integrated SPSS examples for hands-on learning from one large study (for consistency of application throughout the text). - Examples of written results to enable students to learn how the results of these procedures are communicated. - Practical application of the techniques using contemporary studies that will resonate with students.

Table of Contents:
Preface PART I. FOUNDATIONS 1. An Introduction to Multivariate Design 2. Some Fundamental Research Design Concepts 3A. Data Screening 3B. Data Screening Using SPSS PART II. THE INDEPENDENT VARIABLE VARIATE 4A. Bivariate Correlation and Simple Linear Regression 4B. Bivariate Correlation and Simple Linear Regression Using SPSS 5A. Multiple Regression 5B. Multiple Regression Using SPSS 6A. Logistic Regression 6B. Logistic Regression Using SPSS 7A. Discriminant Function Analysis 7B. Two-Group Discriminant Function Analysis Using SPSS PART III. THE DEPENDENT VARIABLE VARIATE 8A. Univariate Comparisons of Means 8B. Univariate Comparisons of Means Using SPSS 9A. MANOVA: Comparing Two Groups 9B. Two-Group MANOVA Using SPSS 10A. MANOVA: Comparing Three or More Groups 10B. MANOVA: Comparing Three or More Groups Using SPSS 11A. MANOVA: Two-Way Factorial 11B. MANOVA: Two-Way Factorial Using SPSS PART IV. THE EMERGENT VARIATE 12A. Principle Components and Factor Analysis 12B. Principle Components and Factor Analysis Using SPSS 13A. Confirmatory Factor Analysis 13B. Confirmatory Factor Analysis Using AMOS PART V. MODEL FITTING 14A. Causal Modeling: Path Analysis and Structural Equation Modeling 14B. Path Analysis Using SPSS and AMOS 15A. Applying a Model to Different Groups 15B. Assessing Model Invariance Between Groups Using AMOS Appendix References Name Index Subject Index About the Authors

About the Author :
Lawrence S. Meyers earned his doctorate in experimental psychology and has been a Professor in the Psychology Department at California State University, Sacramento, for a number of years. He supervises research students and teaches research design courses as well as history of psychology at both the undergraduate and graduate levels. His areas of expertise include test development and validation. Glenn Gamst is Professor and Chair of the Psychology Department at the University of La Verne, where he teaches the doctoral advanced statistics sequence. His research interests include the effects of multicultural variables on clinical outcome. Additional research interests focus on conversation memory and discourse processing. He received his PhD in experimental psychology from the University of Arkansas. A. J. Guarino is a professor of biostatistics at Massachusetts General Hospital, Institute of Health Professions. He is the statistician on numerous National Institutes of Health grants and a reviewer on several research journals. He received his BA from the University of California, Berkeley, and a PhD in statistics and research methodologies from the Department of Educational Psychology, the University of Southern California.