The Essence of Multivariate Thinking: Basic Themes and Methods

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Focusing on the underlying themes that run through most multivariate methods, in this fully updated 3rd edition of The Essence of Multivariate Thinking Dr. Harlow shares the similarities and differences among multiple multivariate methods to help ease the understanding of the basic concepts. The book continues to highlight the main themes that run through just about every quantitative method, describing the statistical features in clear language. Analyzed examples are presented in 12 of the 15 chapters, showing when and how to use relevant multivariate methods, and how to interpret the findings both from an overarching macro- and more specific micro-level approach that includes focus on statistical tests, effect sizes and confidence intervals. This revised 3rd edition offers thoroughly revised and updated chapters to bring them in line with current information in the field, the addition of R code for all examples, continued SAS and SPSS code for seven chapters, two new chapters on structural equation modeling (SEM) on multiple sample analysis (MSA) and latent growth modeling (LGM), and applications with a large longitudinal dataset in the examples of all methods chapters. Of interest to those seeking clarity on multivariate methods often covered in a statistics course for first-year graduate students or advanced undergraduates, this book will be key reading and provide greater conceptual understanding and clear input on how to apply basic and SEM multivariate statistics taught in psychology, education, human development, business, nursing, and other social and life sciences.

Author(s): Lisa L. Harlow
Series: Multivariate Applications Series
Edition: 3
Publisher: Routledge
Year: 2023

Language: English
Pages: 388
City: New York

Cover
Endorsement
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
List of figures
List of tables
About the Author
Acknowledgements
Preface to the Third Edition
PART I: Overview
1. Introduction and Multivariate Themes
2. Background Considerations
PART II: Intermediate Multivariate Methods with One Continuous Outcome
3. Multiple Regression
4. Analysis of Covariance
PART III: Multivariate Group Methods with Categorical Variable(s)
5. Multivariate Analysis of Variance
6. Discriminant Function Analysis
7. Logistic Regression
PART IV: Multivariate Dimensional Methods with Continuous Variables
8. Principal Components and Factor Analysis
PART V: Structural Equation Modeling
9. Structural Equation Modeling
10. Path Analysis
11. Confirmatory Factor Analysis
12. Latent Variable Modeling
13. Multiple Sample Analysis
14. Latent Growth Modeling
PART VI: Summary
15. Integration of Multivariate Methods
Appendix A
Appendix B
Index