000			04868nam a2200145Ia 4500
020			_a9781593852740
040			_cCUS
082			_a150.15195354 _bBRO/C
100			_aBrown,Timothy A.
245		0	_aConfirmatory Factor Analysis for Applied Research/ _cTimothy A. Brown
260			_aNew York: _bThe Guilford Press, _c2006.
300			_a474p.p.
505			_a1 • Introduction Uses of Confirmatory Factor Analysis Psychometric Evaluation of Test Instruments Construct Valiclation Method Effects Measurement Invariance Evaluation Why a Book on CPA? 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 Eactor Extraction Factor Selection Factor Rotation Eactor Scores Summary 3 • Introduction to CFA Similarities and Differences of EFA and CFA Common Eactor Model Standardized and Unstandardized Solutions Indicator Cross-Loadings/Model Parsimony Unique Variances Model Comparison Purposes and /Vdvantages of CPA Parameters of a CPA Model Pundamental Equations of a CPA Model CPA Model Identification Scaling the Latent Variable Statistical Identification Gtiidelines for Model Identification Estimation of CPA Model Parameters Illustration Descriptive Goodness-of-Pit Indices Absolute Fit Parsimony Correction Comparative Fit Cuidelines for Interpreting Coodness-of-Fit Indices Summary Appendix 3.1. Communalilics, Model-Implied Correlalion.s, and Factor Correlations in EPA and CPA Appendix 3.2. Obtainitig a Solution for a Just-Identified Factor Model Appendix 3.3. Hand Calculation of for the Figure 3.8 Path Model 4 • Specification and Interpretation of CPA Models An Applied Example of a CPA Measurement Model Model Specification Sidistantive Justification Defining the Metric of Latent Variables Data Screening and Selection of the Pitting Punction Running the CPA Analysis Model Evaluation Overall Coodness of Fit Localized Areas of Strain Residuals Modification Indices Unnecessary Parametei s Intelprctability. Size, and Statistical Significance of the Parameter Estimates Interpretation and Calculation of CPA Model Parameter Estimates CPA Models with Single Indicators Reporting a Cf*A Study Sutnmary Appendix 4.1 . Model Identification Affects the Standard Errors of the Paratncter Pstitrtaics Appendix 4.2. Cioodncss of Model Fit Docs Not Pnsutc Meaningful Paratncter Estimates Appendix 4.3. Example Report of the fwo-Factor CPA Model of Ncuroticism 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 CEA Solutions Summary 6 • CFA of Muititraif^Multimethod Matrices Correlated versus Random Measurement Error Revisited The Multitrait-Multimethod Matrix CEA Approaches to Analyzing the MTMM Matrix Correlated Methods Models Correlated Unicjuencss 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 Croup Congeneric, Tau-Eqiiivalcnt, and Parallel Indicators Longitudinal Measurement Invariance CFA in Multiple Croups 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 CPA Models: Higher-Order Factor Analysis, Scale Reliability Evaluation, and Formative Indicators Higher-Order Factor Analysis Sccond-Ordcr Factor Analysis Schmid-Lciman Transformation Scale Reliability Estimation Point Estimation oj Scale Reliability Standard Error and Intenal Estimation of Scale Reliahilitv Models with Formative Indicators Summar\' • Data issues in CPA: Missing, Non-Normal, and Categorical Data CPA with Missing Data Mechanisms of Missing Data Conventional Approaches to Missing Data Recommended Missing Data Strategies CPA with Non-Normal or Categorical Data Non-Normal, Continuous Data Categorical Data Othet Potential Remedies for Indicator Non-Normality Summary 10 • Statistical Power and Sample Size Overview Satorra-Saris Method Monte Carlo Approach Summary and Future Directions in CPA Appendix 10.1. Monte Carlo Simulation in Greater Depth: Data Generation
942			_cWB16
999			_c172794 _d172794