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Emphasizing practical and theoretical aspects of confirmatory factor analysis (CFA) rather than mathematics or formulas, Timothy A. Brown uses rich examples derived from the psychology, management, and sociology literatures to provide in-depth treatment of the concepts, procedures, pitfalls, and extensions of CFA methodology. Chock full of useful advice and tables that outline the procedures, the text shows readers how to conduct exploratory factor analysis (EFA) and understand similarities to and differences from CFA; formulate, program, and interpret CFA models using popular latent variable software packages such as LISREL, Mplus, Amos, EQS, and SAS/CALIS; and report results from a CFA study. Also covered are extensions of CFA to traditional IRT analysis, methods for determining necessary sample sizes, and new CFA modeling possibilities, including multilevel factor models and factor mixture models. Special features include a companion Web page offering data and program syntax files for many of the research examples so that readers can practice the procedures described in the book with real data. The Web page also includes links to additional CFA-related resources.

Tabla de contenidos provista por Syndetics

• 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 the CFA Analysis Model
• Evaluation Overall Goodness of Fit
• Localized Areas of Strain
• Residuals Modification
• Indices Unnecessary Parameters
• 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 CFA 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
• EFA in the CFA Framework 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
• CFA in Multiple Groups
• Overview of Multiple-Groups Solutions
• Multiple-Groups CFA
• Selected Issues in Single- and Multiple-Groups CFA
• Invariance Evaluation MIMIC Models (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 Scale
• Reliability Estimation Point Esti

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CHOICE Review

Brown's comprehensive guide to confirmatory factor analysis (CFA) fills a gap in the literature of statistical modeling methods for the social and behavioral sciences. Brown (psychology, Boston Univ.) provides a clear, approachable explanation of the appropriate application and power of CFA and distinguishes it from the more heavily documented, but less frequently used, exploratory factor analysis (EFA) and structural equation modeling (SEM). Further, he demonstrates CFA's considerable strengths over the other methods. Applied researchers will learn to understand and use CFA in evaluation of test instruments (during scale development), construct validation, measurement method effects, and variance evaluation, and they will all profit from procedural examples in several social science disciplines and in a wide variety of statistical software packages. Brown cuts to the chase by giving just what the applied researcher needs to use CFA accurately. He presents core materials succinctly and isolates subtopics and examples in appendixes following the relevant chapter. The explanation of power and sample sizes, which is particularly valuable, has broader application beyond CFA. Students will appreciate the section on reporting CFA results. ^BSumming Up: Recommended. Graduate students, researchers, and professionals. A. C. Moore University of Massachusetts at Amherst

Notas de autor provistas por Syndetics

Timothy A. Brown is a professor in the Department of Psychology at Boston University (BU), and Director of Research at BU's Center for Anxiety and Related Disorders. He has published extensively in the areas of the classification of anxiety and mood disorders, psychometrics, and methodological advances in social sciences research.