Cover

Contents

Preface

Acknowledgments

Part I - Background

Chapter 1 - What Does It Mean to Model Hypothesized Causal Processes With Nonexperimental Data?

Methods for Structural Equation Analyses

Overview

Chapter 2 - History and Logic of Structural Equation Modeling

History

Sewell Wight

Path Analysis in the Social Sciences

Unidirectional Flow Models

Moving Beyond Path Analysis in Structural Equation Modeling Research

Why Use Structural Equation Modeling Techniques?

Part II - Basic Approaches to Modeling With Single Observed Measures of Theoretical Variables

Chapter 3 - The Basics: Path Analysis and Partitioning of Variance

Logic of Correlations and Covariances

Decomposing Relationships Between Variables Into Causal and Noncausal Components

Direct Causal Effects

Indirect Causal Effects

Noncausal Relationships Due to Shared Antecedents

Noncausal Unanalyzed Prior Association Relationships

Approaches for Decomposing Effects

Determining Degrees of Freedom of Models

Presenting Partial Regression and Partial Correlation as Path Models

Partial Regression

Partial Correlation

Peer Popularity and Academic Achievement: An Illustration

Chapter 4 - Effects of Collinearity on Regression and Path Analysis

Regression and Collinearity

Illustrating Effects of Collinearity

Confidence Intervals for Correlations

Ridge or Reduced Variance Regression

Chapter 5 - Effects of Random and Nonrandom Error on Path Models

Measurement Error

Background

Specifying Relationships Between Theoretical Variables and Measures

Random Measurement Error

Nonrandom Error

Method Variance and Multitrait-Multimethod Models

Method Variance

Additive Multitrait-Multimethod Models

Nonadditive Multitrait-Multimethod Models

Summary

Chapter 6 - Recursive and Longitudinal Models: Where Causality Goes in More Than One Direction and Where Data Are Collected Over Time

Models With Multidirectional Paths

Logic of Nonrecursive Models

Estimation of Nonrecursive Models

Model Identification

Longitudinal Models

Logic Underlying Longitudinal Models

Terminology of Panel Models

Identification

Stability

Temporal Lags in Panel Models

Growth Across Time in Panel Models

Stability of Causal Processes

Effects of Excluded Variables

Correlation and Regression Approaches for Analyzing Panel Data

Summary

Part III - Factor Analysis and Path Modeling

Chapter 7 - Introducing the Logic of Factor Analysis and Multiple Indicators to Path Modeling

Factor Analysis

Logic of Factor Analysis

Exploratory Factor Analysis

Confirmatory Factor Analysis

Use of Confirmatory Factor Analysis Techniques

Constraining Relations of Observed Measures With Factors

Confirmatory Factor Analysis and Method Factors

The Basic Confirmatory Factor Analysis Path Model for Multitrait-Multimethod Matrices

Confirmatory Factor Analysis Approaches to Multitrait-Multimethod Matrices and Model Identification

Summary of Confirmatory Factor Analysis and Multitrait-Multimethod Models

Initial Testing of Plausibility of Models: Consistency Tests

Number of Indicators and Consistency Tests

Costner's Original Consistency Model

Part IV - Latent Variable Structural Equation Models

Chapter 8 - Putting It All Together: Latent Variable Structural Equation Modeling

The Basic Latent Variable Structural Equation Model

The Measurement Model

Reference Indicators

The Structural Model

An Illustration of Structural Equation Models

Model Specification

Identification

Equations and Matrices

Basic Ideas Underlying Fit/Significance Testing

Individual Parameter Significance

Model Fitting

The Measurement Model

The Structural Model

The Variance/Covariance Matrices

Chapter 9 - Using Latent Variable Structural Equation Modeling to Examine Plausibility of Models

Example 1: A Longitudinal Path Model

Example 2: A Nonrecursive Multiple-Indicator Model

Example 3: A Longitudinal Multiple-Indicator Panel Model

Chapter 10 - Logic of Alternative Models and Significance Tests

Nested Models

Tests of Overall Model Fit

Absolute Indexes

Relative Indexes

Adjusted Indexes

Fit Indexes for Comparing Non-Nested Models

Setting Up Nested Models

Why Models May Not Fit

Illustrating Fit Tests

Chapter 11- Variations on the Basic Latent Variable Structural Equation Model

Analyzing Structural Equation Models When Multiple Populations Are Available

Overview of Methods

Comparing Processes Across Samples

Testing Plausibility of Contraints

Constraints in the Measurement Model

Constraints in the Structural Model

When and How to Impose Equality Constraints

Second-Order Factor Models

All-Y Models

Chapter 12 - Wrapping Up

Criticisms of Structural Equation Modeling Approaches

"Internal" Critics

"External" Critics

Emerging Criticisms

Post Hoc Model Modification

Topics Not Covered

Power Analysis

Nonlinear Relationships

Alternative Estimation Techniques

Analysis of Noncontinuous Variables

Adding Analysis of Means

Multilevel Structural Equation Modeling

Writing Up Papers Containing Structural Equation Modeling Analysis

Selecting a Computer Program to Do Latent Variable Structural Equation Modeling

Appendix A - A Brief Introduction to Matrix Algebra and Structural Equation Modeling

What Is a Matrix?

Matrix Operations

Inverting Matrices

Determinants

Matrices and Rules

Answers to Chapter Discussion Questions

References

Author Index

Subject Index

About the Author