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• Half Title
• Methodology in the Social Sciences
• Title
• Copyright
• Dedication
• Series Editor’s Note
• Preface
• Contents
• 1. Introduction
• Uses of Confirmatory Factor Analysis
• Psychometric Evaluation of Test Instruments
• Construct Validation
• Method Effects
• Measurement Invariance Evaluation
• Why a Book on CFA?
• Coverage of the Book
• Other Considerations
• Summary
• 2. The Common Factor Model and Exploratory Factor Analysis
• Overview of the Common Factor Model
• Procedures of EFA
• Factor Extraction
• Factor Selection
• Factor Rotation
• Factor Scores
• Summary
• 3. Introduction to CFA
• Similarities and Differences of EFA and CFA
• Common Factor Model
• Standardized and Unstandardized Solutions
• Indicator Cross‑Loadings/Model Parsimony
• Unique Variances
• Model Comparison
• Purposes and Advantages of CFA
• Parameters of a CFA Model
• Fundamental Equations of a CFA Model
• CFA Model Identification
• Scaling the Latent Variable
• Statistical Identification
• Guidelines for Model Identification
• Estimation of CFA Model Parameters
• Illustration
• Descriptive Goodness‑of‑Fit Indices
• Absolute Fit
• Parsimony Correction
• Comparative Fit
• Guidelines for Interpreting Goodness‑of‑Fit Indices
• Summary
• Appendix 3.1. Communalities, Model-Implied Correlations, and Factor Correlations in EFA and CFA
• Appendix 3.2. Obtaining a Solution for a Just-Identified Factor Model
• Appendix 3.3. Hand Calculation of FML for the Figure 3.8 Path Model
• 4. Specification and Interpretation of CFA Models
• An Applied Example of a CFA Measurement Model
• Model Specification
• Substantive Justification
• Defining the Metric of Latent Variables
• Data Screening and Selection of the Fitting Function
• Running CFA in Different Software Programs
• Model Evaluation
• Overall Goodness of Fit
• Localized Areas of Strain
• Interpretability, Size, and Statistical Significance of the Parameter Estimates
• Interpretation and Calculation of CFA Model Parameter Estimates
• CFA Models with Single Indicators
• Reporting a CFA Study
• Summary
• Appendix 4.1. Model Identification Affects the Standard Errors of the Parameter Estimates
• Appendix 4.2. Goodness of Model Fit Does Not Ensure Meaningful Parameter Estimates
• Appendix 4.3. Example Report of the Two-Factor CFA Model of Neuroticism and Extraversion
• 5. Model Revision and Comparison
• Goals of Model Respecification
• Sources of Poor‑Fitting CFA Solutions
• Number of Factors
• Indicators and Factor Loadings
• Correlated Errors
• Improper Solutions and Nonpositive Definite Matrices
• Intermediate Steps for Further Developing a Measurement Model for CFA
• EFA in the CFA Framework
• Exploratory SEM
• Model Identification Revisited
• Equivalent CFA Solutions
• Summary
• 6. CFA of Multitrait–Multimethod Matrices
• Correlated versus Random Measurement Error Revisited
• The Multitrait –Multimethod Matrix
• CFA Approaches to Analyzing the MTMM Matrix
• Correlated Methods Models
• Correlated Uniqueness Models
• Advantages and Disadvantages of Correlated Methods and Correlated Uniqueness Models
• Other CFA Parameterizations of MTMM Data
• Consequences of Not Modeling Method Variance and Measurement Error
• Summary
• 7. CFA with Equality Constraints, Multiple Groups, and Mean Structures
• Overview of Equality Constraints
• Equality Constraints within a Single Group
• Congeneric, Tau‑Equivalent, and Parallel Indicators
• Longitudinal Measurement Invariance
• The Effects Coding Approach to Scaling Latent Variables
• CFA in Multiple Groups
• Overview of Multiple‑Groups Solutions
• Multiple‑Groups CFA
• Selected Issues in Single‑ and Multiple‑Groups CFA Invariance Evaluation
• MIMIC Modeling (CFA with Covariates)
• Summary
• Appendix 7.1. Reproduction of the Observed Variance–Covariance Matrix with Tau-Equivalent Indicato
• 8. Other Types of CFA Models: Higher‑Order Factor Analysis, Scale Reliability Evaluation, and Form
• Higher‑Order Factor Analysis
• Second‑Order Factor Analysis
• Schmid–Leiman Transformation
• Bifactor Models
• Scale Reliability Estimation
• Point Estimation of Scale Reliability
• Standard Error and Interval Estimation of Scale Reliability
• Models with Formative Indicators
• Summary
• 9. Data Issues in CFA: Missing, Non‑Normal, and Categorical Data
• CFA with Missing Data
• Mechanisms of Missing Data
• Conventional Approaches to Missing Data
• Recommended Strategies for Missing Data
• CFA with Non‑Normal or Categorical Data
• Non‑Normal, Continuous Data
• Categorical Data
• Other Potential Remedies for Indicator Non‑Normality
• Summary
• 10. Statistical Power and Sample Size
• Overview
• Satorra–Saris Method
• Monte Carlo Approach
• Summary
• Appendix 10.1. Monte Carlo Simulation in Greater Depth: Data Generation
• 11. Recent Developments Involving CFA Models
• Bayesian CFA
• Bayesian Probability and Statistical Inference
• Priors in CFA
• Applied Example of Bayesian CFA
• Bayesian CFA: Summary
• Multilevel CFA
• Summary
• Appendix 11.1. Numerical Example of Bayesian Probability
• References
• Author Index
• Subject Index
• About the Author
• Blank Page