The essence of multivariate thinking : basic themes and methods
著者
書誌事項
The essence of multivariate thinking : basic themes and methods
（Multivariate applications book series）
Routledge, 2023
3rd ed
- : hbk
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
I. OVERVIEW
Chapter 1: Introduction and Multivariate Themes
Chapter 2: Background Themes
II. INTERMEDIATE MULTIVARIATE METHODS WITH ONE CONTINUOUS OUTCOME
Chapter 3: Multiple Regression
Chapter 4: Analysis of Covariance
III. MULTIVARIATE GROUP METHODS WITH CATEGORICAL VARIABLE(S)
Chapter 5. Multivariate Analysis of Variance
Chapter 6: Discriminant Function Analysis
Chapter 7: Logistic Regression
IV. MULTIVARIATE DIMENSIONAL METHODS WITH CONTINUOUS VARIABLES
Chapter 8: Principal Components and Factor Analysis
V: STRUCTURAL EQUATION MODELING
Chapter 9: Structural Equation Modeling
Chapter 10: Path Analysis
Chapter 11: Confirmatory Factor Analysis
Chapter 12: Latent Variable Modeling
Chapter 13: Multiple Sample Analysis
Chapter 14: Latent Growth Modeling
VI: SUMMARY
Chapter 15: Integration of Multivariate Methods
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