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• Series Editor’s Introduction
• About the Author
• Acknowledgments
• 1: Introduction to Factor Analysis
• Latent and Observed Variables
• The Importance of Theory in Doing Factor Analysis
• Comparison of Exploratory and Confirmatory Factor Analysis
• EFA and Other Multivariate Data Reduction Techniques
• A Brief Word About Software
• Outline of the Book
• 2: Mathematical Underpinnings of Factor Analysis
• Correlation and Covariance Matrices
• The Common Factor Model
• Correspondence Between the Factor Model and the Covariance Matrix
• Eigenvalues
• Error Variance and Communalities
• Summary
• 3: Methods of Factor Extraction in Exploratory Factor Analysis
• Eigenvalues, Factor Loadings, and the Observed Correlation Matrix
• Maximum Likelihood
• Principal Axis Factoring
• Principal Components Analysis
• Principal Components versus Factor Analysis
• Other Factor Extraction Methods
• Example
• Summary
• 4: Methods of Factor Rotation
• Simple Structure
• Orthogonal Versus Oblique Rotation Methods
• Common Orthogonal Rotations
• Varimax Rotation
• Quartimax Rotation
• Equamax Rotation
• Common Oblique Rotations
• Promax Rotation
• Oblimin
• Geomin Rotation
• Target Factor Rotation
• Bifactor Rotation
• Example
• Deciding Which Rotation to Use
• Summary
• Appendix
• 5: Methods for Determining the Number of Factors to Retain in Exploratory Factor Analysis
• Scree Plot and Eigenvalue Greater Than 1 Rule
• Objective Methods Based on the Scree Plot
• Eigenvalues and the Proportion of Variance Explained
• Residual Correlation Matrix
• Chi-Square Goodness of Fit Test for Maximum Likelihood
• Parallel Analysis
• Minimum Average Partial
• Very Simple Structure
• Example
• Summary
• 6: Final Issues in Factor Analysis
• Proper Reporting Practices for Factor Analysis
• Factor Scores
• Power Analysis and A Priori Sample Size Determination
• Dealing With Missing Data
• Exploratory Structural Equation Modeling
• Multilevel EFA
• Summary
• References
• Index