Confirmatory Factor Analysis for Applied Research Second Edition by Timothy Brown 9781462515363 1462515363

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ISBN 10: 1462515363
ISBN 13: 9781462515363 
Author: Timothy A. Brown

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.

Confirmatory Factor Analysis for Applied Research Second Table of 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

Appendices:

• 3.1. Communalities, Model-Implied Correlations, and Factor Correlations in EFA and CFA

• 3.2. Obtaining a Solution for a Just-Identified Factor Model

• 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

Appendices:

• 4.1. Model Identification Affects the Standard Errors of the Parameter Estimates

• 4.2. Goodness of Model Fit Does Not Ensure Meaningful Parameter Estimates

• 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

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