Through the use of careful narrative explanation, Basics of Structural Equation Modeling describes the logic underlying structural equation modeling (SEM) approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data. Throughout the book, Maruyama uses a single data set (the relationships between peer acceptance and achievement) to demonstrate a variety of techniques ranging from path analysis to panel analysis to confirmatory analysis to latent variable structural equation modeling. In addition, Maruyama provides carefully constructed exercises both within and at the end of chapters so that readers can apply the material that they have just read. This book will provide readers with a good understanding of how and why structural equation approaches have come to be used.


TABLE OF CONTENTS

Preface
Acknowledgments
1 What Does It Mean to Model Hypothesized Causal Processes With Nonexperimental Data?
2 History and Logic of Structural Equation Modeling
3 The Basics: Path Analysis and Partitioning of Variance
4 Effects of Collinearity on Regression and Path Analysis
5 Effects of Random and Nonrandom Error on Path Models
6 Recursive and Longitudinal Models: Where Causality Goes in More Than One Direction and Where Data Are Collected Over Time
7 Introducing the Logic of Factor Analysis and Multiple Indicators to Path Modeling
8 Putting It All Together: Latent Variable Structural Equation Modeling
9 Using Latent Variable Structural Equation Modeling to Examine Plausibility of Models
10 Logic of Alternative Models and Significance Tests
11 Variations on the Basic Latent Variable Structural Equation Model
12 Wrapping Up

App. A A Brief Introduction to Matrix Algebra and Structural Equation Modeling
Answers to Chapter Discussion Questions
References
Author Index
Subject Index
About the Author