Applied Multivariate Research Design and Interpretation

ISBN-10: 1412904129
ISBN-13: 9781412904124
Edition: 2006
List price: $129.00
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Description: 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  More...

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Book details

List price: $129.00
Copyright year: 2006
Publisher: SAGE Publications, Incorporated
Publication date: 9/8/2005
Binding: Hardcover
Pages: 760
Size: 7.25" wide x 9.00" long x 0.75" tall
Weight: 3.322

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 behavioral 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 gearstoward the needs, level of sophistication, and interest in multivariate methodology of students in these applied programs that need to focus on design and interpretation rather than the intricacies of specific computations. nbsp; 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.

Larry 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 level. 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. He received his Ph.D. from the University of Arkansas in experimental psychology. His research interests include the effects of multicultural variables on clinical outcome. Additional research interests focus conversation memory and discourse processing.

Preface
Foundations
An Introduction to Multivariate Design
Some Fundamental Research Design Concepts
Data Screening
Data Screening Using SPSS
The Independent Variable Variate
Bivariate Correlation and Simple Linear Regression
Bivariate Correlation and Simple Linear Regression Using SPSS
Multiple Regression
Multiple Regression Using SPSS
Logistic Regression
Logistic Regression Using SPSS
Discriminant Function Analysis
Two-Group Discriminant Function Analysis Using SPSS
The Dependent Variable Variate
Univariate Comparisons of Means
Univariate Comparisons of Means Using SPSS
MANOVA: Comparing Two Groups
Two-Group MANOVA Using SPSS
MANOVA: Comparing Three or More Groups
MANOVA: Comparing Three or More Groups Using SPSS
MANOVA: Two-Way Factorial
MANOVA: Two-Way Factorial Using SPSS
The Emergent Variate
Principle Components and Factor Analysis
Principle Components and Factor Analysis Using SPSS
Confirmatory Factor Analysis
Confirmatory Factor Analysis Using AMOS
Model Fitting
Causal Modeling: Path Analysis and Structural Equation Modeling
Path Analysis Using SPSS and AMOS
Applying a Model to Different Groups
Assessing Model Invariance Between Groups Using AMOS
Appendix
References
Name Index
Subject Index
About the Authors

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