Efnisyfirlit

• Cover Page
• Half-Title Page
• Seies Page
• Title Page
• Copyright Page
• Brief Contents
• Table of Contents
• Preface
• Acknowledgments
• About the Author
• Section I: Introduction
• Chapter 1 Structural Equation Modeling: The Basics
• Key Concepts
• What Is Structural Equation Modeling?
• Basic Concepts
• Latent versus Observed Variables
• Exogenous versus Endogenous Latent Variables
• The Factor Analytic Model
• The Full Latent Variable Model
• General Purpose and Process of Statistical Modeling
• The General Structural Equation Model
• Symbol Notation
• The Path Diagram
• Structural Equations
• Nonvisible Components of a Model
• Basic Composition
• The Formulation of Covariance and Mean Structures
• Notes
• Chapter 2 Using the Amos Program
• Key Concepts
• Model Specification Using Amos Graphics (Example 1)
• Amos Modeling Tools
• The Hypothesized Model
• Drawing the Path Diagram
• Model Specification Using Amos Tables View (Example 1)
• Understanding the Basic Components of Model 1
• The Concept of Model Identification
• Model Specification Using Amos Graphics (Example 2)
• The Hypothesized Model
• Drawing the Path Diagram
• Model Specification Using Amos Tables View (Example 2)
• Model Specification Using Amos Graphics (Example 3)
• The Hypothesized Model
• Drawing the Path Diagram
• Changing the Amos Default Color for Constructed Models
• Model Specification Using Amos Tables View (Example 3)
• Notes
• Section II: Single-Group Analyses
• Confirmatory Factor Analytic Models
• Chapter 3 Application 1: Testing the Factorial Validity of a Theoretical Construct (First-Order CFA Model)
• Key Concepts
• The Hypothesized Model
• Hypothesis 1: Self-concept is a 4-Factor Structure
• Modeling with Amos Graphics
• Model Specification
• Data Specification
• Calculation of Estimates
• Amos Text Output: Hypothesized 4-Factor Model
• Model Summary
• Model Variables and Parameters
• Model Evaluation
• Parameter Estimates
• Model as a Whole
• Model Misspecification
• Post Hoc Analyses
• Hypothesis 2: Self-concept is a 2-Factor Structure
• Selected Amos Text Output: Hypothesized 2-Factor Model
• Hypothesis 3: Self-concept is a 1-Factor Structure
• Modeling with Amos Tables View
• Notes
• Chapter 4 Application 2: Testing the Factorial Validity of Scores from a Measurement Scale (First-Order CFA Model)
• Key Concepts
• Modeling with Amos Graphics
• The Measuring Instrument under Study
• The Hypothesized Model
• Selected Amos Output: The Hypothesized Model
• Model Evaluation
• Post Hoc Analyses
• Model 2
• Selected Amos Output: Model 2
• Model 3
• Selected Amos Output: Model 3
• Model 4
• Selected Amos Output: Model 4
• Comparison with Robust Analyses Based on the Satorra-Bentler Scaled Statistic
• Modeling with Amos Tables View
• Notes
• Chapter 5 Application 3: Testing the Factorial Validity of Scores from a Measurement Scale (Second-Order CFA Model)
• Key Concepts
• The Hypothesized Model
• Modeling with Amos Graphics
• Selected Amos Output File: Preliminary Model
• Selected Amos Output: The Hypothesized Model
• Model Evaluation
• Estimation Based on Continous Versus Categorical Data
• Categorical Variables Analyzed as Continuous Variables
• Categorical Variables Analyzed as Categorical Variables
• The Amos Approach to Analysis of Categorical Variables
• What is Bayesian Estimation?
• Application of Bayesian Estimation
• Modeling with Amos Tables View
• Note
• Full Latent Variable Model
• Chapter 6 Application 4: Testing the Validity of a Causal Structure
• Key Concepts
• The Hypothesized Model
• Modeling with Amos Graphics
• Formulation of Indicator Variables
• Confirmatory Factor Analyses
• Selected Amos Output: Hypothesized Model
• Model Assessment
• Post Hoc Analyses
• Selected Amos Output: Model 2
• Model Assessment
• Selected Amos Output: Model 3
• Model Assessment
• Selected Amos Output: Model 4
• Model Assessment
• Selected Amos Output: Model 5
• Model Assessment
• Selected Amos Output: Model 6
• Model Assessment
• The Issue of Model Parsimony
• Selected Amos Output: Model 7 (Final Model)
• Model Assessment
• Parameter Estimates
• Modeling with Amos Tables View
• Notes
• Section III: Multiple-Group Analyses
• Confirmatory Factor Analytic Models
• Chapter 7 Application 5: Testing Factorial Invariance of Scales from a Measurement Scale (First-Order CFA Model)
• Key Concepts
• Testing For Multigroup Invariance
• The General Notion
• The Testing Strategy
• The Hypothesized Model
• Establishing Baseline Models: The General Notion
• Establishing the Baseline Models: Elementary and Secondary Teachers
• Modeling with Amos Graphics
• Hierarchy of Steps in Testing Multigroup Invariance
• I. Testing for Configural Invariance
• Selected Amos Output: The Configural Model (No Equality Constraints Imposed)
• II. Testing for Measurement and Structural Invariance: The Specification Process
• III. Testing for Measurement and Structural Invariance: Model Assessment
• Testing For Multigroup Invariance: The Measurement Model
• Model Assessment
• Testing For Multigroup Invariance: The Structural Model
• Notes
• Chapter 8 Application 6: Testing Invariance of Latent Mean Structures (First-Order CFA Model)
• Key Concepts
• Basic Concepts Underlying Tests of Latent Mean Structures
• Estimation of Latent Variable Means
• The Hypothesized Model
• The Baseline Models
• Modeling with Amos Graphics
• The Structured Means Model
• Testing for Latent Mean Differences
• The Hypothesized Multigroup Model
• Steps in the Testing Process
• Selected Amos Output: Model Summary
• Selected Amos Output: Goodness-of-fit Statistics
• Selected Amos Output: Parameter Estimates
• Notes
• Full Latent Variable Model
• Chapter 9 Application 7: Testing Invariance of a Causal Structure (Full Structural Equation Model)
• Key Concepts
• Cross-Validation in Covariance Structure Modeling
• Testing for Invariance across Calibration/Validation Samples
• The Hypothesized Model
• Establishing a Baseline Model
• Modeling with Amos Graphics
• Testing for the Invariance of Causal Structure Using the Automated Multigroup Approach
• Selected Amos Output: Goodness-of-fit Statistics for Comparative Tests of Multigroup Invariance
• Section IV: Other Important Applications
• Chapter 10 Application 8: Testing Evidence of Construct Validity: The Multitrait-Multimethod Model
• Key Concepts
• The Correlated Traits-Correlated Methods Approach to MTMM Analyses
• Model 1: Correlated Traits-Correlated Methods
• Model 2: No Traits-Correlated Methods
• Model 3: Perfectly Correlated Traits-Freely Correlated Methods..
• Model 4: Freely Correlated Traits-Uncorrelated Methods
• Testing for Evidence of Convergent and Discriminant Validity: MTMM Matrix-level Analyses
• Comparison of Models
• Evidence of Convergent Validity
• Evidence of Discriminant Validity
• Testing for Evidence of Convergent and Discriminant Validity: MTMM Parameter-level Analyses
• Examination of Parameters
• Evidence of Convergent Validity
• Evidence of Discriminant Validity
• The Correlated Uniquenesses Approach to MTMM Analyses
• Model 5: Correlated Uniqueness Model
• Notes
• Chapter 11 Application 9: Testing Change Over Time: The Latent Growth Curve Model
• Key Concepts
• Measuring Change in Individual Growth over Time: The General Notion
• The Hypothesized Dual-domain LGC Model
• Modeling Intraindividual Change
• Modeling Interindividual Differences in Change
• Testing Latent Growth Curve Models: A Dual-Domain Model
• The Hypothesized Model
• Selected Amos Output: Hypothesized Model
• Testing Latent Growth Curve Models: Gender as a Time-invariant Predictor of Change
• Notes
• Section V: Other Important Topics
• Chapter 12 Application 10: Use of Bootstrapping in Addressing Nonnormal Data
• Key Concepts
• Basic Principles Underlying the Bootstrap Procedure
• Benefits and Limitations of the Bootstrap Procedure
• Caveats Regarding the Use of Bootstrapping in SEM
• Modeling with Amos Graphics
• The Hypothesized Model
• Characteristics of the Sample
• Applying the Bootstrap Procedure
• Selected Amos Output
• Parameter Summary
• Assessment of Normality
• Parameter Estimates and Standard Errors
• Note
• Chapter 13 Application 11: Addressing the Issues of Missing Data
• Key Concepts
• Basic Patterns of Missing Data
• Common Approaches to Handling Incomplete Data
• Ad Hoc Approaches to Handling Missing Data (Not recommended)
• Theory-based Approaches to Handling Missing Data (Recommended)
• The Amos Approach to Handling Missing Data
• Modeling with Amos Graphics
• The Hypothesized Model
• Selected Amos Output: Parameter and Model Summary Information
• Selected Amos Output: Parameter Estimates
• Selected Amos Output: Goodness-of-fit Statistics
• Note
• References
• Author Index
• Subject Index