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An Introduction to Applied Multivariate Analysis, 1st Edition By Tenko Raykov, George A. Marcoulides

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Pages: 498
File size: 2.29 MB
Format: Digital PDF
Course: Behavioural Science
Category: eBook[PDF]

About this ebook

The 1st edition of An Introduction to Applied Multivariate Analysis by Tenko Raykov and George A. Marcoulides covers the following topics across 13 core chapters: [1]

Foundational Concepts & Data Preparation

Chapter 1: Introduction to Multivariate Statistics — Outlines the basic principles of multivariate analysis, exploring the critical relationships between independent and dependent variables while drawing analogies to familiar univariate procedures. [1, 2, 3]
Chapter 2: Elements of Matrix Theory — Provides the fundamental mathematical building blocks and matrix algebra necessary to understand how multivariate data is structured and processed. [1, 2]
Chapter 3: Data Screening and Preliminary Analyses — Details essential, practical steps for assessing data quality, addressing statistical assumptions, and identifying potential pitfalls before launching into complex models. [1, 2]

Examining Group Differences & Variation

Chapter 4: Multivariate Analysis of Group Differences — Focuses on techniques like MANOVA (Multivariate Analysis of Variance) to analyze how distinct groups differ across multiple continuous variables simultaneously. [1, 2]
Chapter 5: Repeated Measure Analysis of Variance — Explores methods for handling data where measurements are taken repeatedly from the same subjects over time or under varying conditions. [1, 2]
Chapter 6: Analysis of Covariance — Covers how to control for extraneous or concomitant variables (covariates) to better isolate group effects. [1, 2]

Dimensionality Reduction & Factor Models

Chapter 7: Principal Component Analysis (PCA) — Examines data reduction techniques to streamline a high number of variables into a smaller set of uncorrelated components.
Chapter 8: Exploratory Factor Analysis (EFA) — Looks at analyzing underlying relationships among survey or test items to discover the fundamental unobserved dimensions (constructs) driving the data.
Chapter 9: Confirmatory Factor Analysis (CFA) — Introduces latent variable modeling to rigorously test and confirm whether a predefined structural hypothesis matches the data. [1, 2, 3, 4]

Classification & Association

Chapter 10: Discriminant Function Analysis — Identifies how to predict group membership or classify observations based on a set of predictor variables.
Chapter 11: Canonical Correlation Analysis — Examines structural connections and linear combinations that best predict relationships between two entire sets of variables. [1, 2, 3]

Advanced & Contemporary Topics

Chapter 12: An Introduction to the Analysis of Missing Data — Expands beyond traditional textbook scope by detailing modern approaches to missingness mechanisms and how to statistically navigate missing records safely.
Chapter 13: Multivariate Analyses of Change Processes — Discusses longitudinal analysis and modeling transformations over extended intervals, explicitly tailored for behavioral, educational, and social sciences. [1, 2, 3]