Confirmatory Factor Analysis for Applied Research

ISBN-10: 1593852746
ISBN-13: 9781593852740
Edition: 2006
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Book details

Copyright year: 2006
Publisher: Guilford Publications
Publication date: 3/30/2006
Binding: Paperback
Pages: 475
Size: 6.00" wide x 9.00" long x 1.00" tall
Weight: 1.364
Language: English

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.

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
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
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
Communalities, Model-Implied Correlations, and Factor Correlations in EFA and CFA
Appendix
Obtaining a Solution for a Just-Identified
Factor Model
Appendix
Hand Calculation of FML for the Figure 3.8 Path Model
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
Model Identification Affects the Standard
Errors of the Parameter Estimates
Appendix
Goodness of Model Fit
Does Not Ensure Meaningful Parameter Estimates
Appendix
Example Report of the Two-Factor
CFA Model of Neuroticism and Extraversion
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
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
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
Reproduction of the Observed Variance-
Covariance Matrix with Tau-Equivalent
Indicators of Auditory Memory
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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