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

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