9781462515363

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With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities and differences between CFA and exploratory factor analysis (EFA); and report results from a CFA study. It is filled with useful advice and tables that outline the procedures. The companion website (www.guilford.com/brown3-materials) offers data and program syntax files for most of the research examples, as well as links to CFA-related resources.

New to This Edition
*Updated throughout to incorporate important developments in latent variable modeling.
*Chapter on Bayesian CFA and multilevel measurement models.
*Addresses new topics (with examples): exploratory structural equation modeling, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables.
*Utilizes the latest versions of major latent variable software packages.

Timothy A. Brown, PsyD, is Professor in the Department of Psychology and Director of Research at the Center for Anxiety and Related Disorders at Boston University. He has published extensively in the areas of the classification of anxiety and mood disorders, the psychopathology and risk factors of emotional disorders, psychometrics, and applied research methods. In addition to conducting his own grant-supported research, Dr. Brown serves as a statistical investigator or consultant on numerous federally funded research projects. He has been on the editorial boards of several scientific journals, including a longstanding appointment as Associate Editor of the Journal of Abnormal Psychology.

l. 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 Indicators of Auditory Memory
8. Other Types of CFA Models: Higher-Order Factor Analysis, Scale Reliability
                  Evaluation, and Formative Indicators
     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

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Confirmatory Factor Analysis for Applied Research

Confirmatory Factor Analysis for Applied Research

1462515363

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