ABSTRACT

Emphasizing causation as a functional relationship between variables, this book provides comprehensive coverage on the basics of SEM. It takes readers through the process of identifying, estimating, analyzing, and evaluating a range of models. The author discusses the history and philosophy of causality and its place in science and presents graph theory as a tool for the design and analysis of causal models. He explains how the algorithms in SEM are derived and how they work, covers various indices and tests for evaluating the fit of structural equation models to data, and explores recent research in graph theory, path tracing rules, and model evaluation.

chapter 1|10 pages

Introduction

chapter 3|48 pages

Causation

chapter 4|8 pages

Graph Theory for Causal Modeling

chapter 5|20 pages

Structural Equation Models

chapter 6|16 pages

Identification

chapter 7|32 pages

Estimation of Parameters

chapter 8|32 pages

Designing SEM Studies

chapter 9|22 pages

Confirmatory Factor Analysis

chapter 10|14 pages

Equivalent Models

chapter 11|6 pages

Instrumental Variables

chapter 12|8 pages

Multilevel Models

chapter 13|16 pages

Longitudinal Models

chapter 14|22 pages

Nonrecursive Models

chapter 15|88 pages

Model Evaluation