Harlow Lisa L. The Essence of Multivariate Thinking: Basic Themes and Methods

3rd edition. — Routledge, 2023. — 389 p. — (Multivariate Applications Series). — ISBN 978-0-367-21970-3.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.Overview
Introduction and Multivariate Themes
Background Considerations
Intermediate Multivariate Methods with One Continuous Outcome
Multiple Regression
Analysis of Covariance
Multivariate Group Methods with Categorical Variable(s)
Multivariate Analysis of Variance
Discriminant Function Analysis
Logistic Regression
Multivariate Dimensional Methods with Continuous Variables
Principal Components and Factor Analysis
Structural Equation Modeling
Structural Equation Modeling
Path Analysis
Confirmatory Factor Analysis
Latent Variable Modeling
Multiple Sample Analysis
Latent Growth Modeling
Summary
Integration of Multivariate Methods