Linear Causal Modeling with Structural Equations

by Stanley A. Mulaik

Overview

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.

Product Details

ISBN-13:	9781040204214
Publisher:	CRC Press
Publication date:	06/16/2009
Series:	Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Sold by:	Barnes & Noble
Format:	eBook
Pages:	468
File size:	3 MB

About the Author

Stanley A. Mulaik is Professor Emeritus in the School of Psychology at the Georgia Institute of Technology.

Table of Contents

Introduction. Mathematical Foundations for Structural Equation Modeling. Causation. Graph Theory for Causal Modeling. Structural Equation Models. Identification. Estimation of Parameters. Designing SEM Studies. Confirmatory Factor Analysis. Equivalent Models. Instrumental Variables. Multilevel Models. Longitudinal Models. Nonrecursive Models. Model Evaluation. Polychoric Correlation and Polyserial Correlation. References. Index.

What People are Saying About This

From the Publisher

The book is written by one of the most prominent researchers in the field of structural equation models (SEM). … It is primarily a useful textbook for graduate students but could also be very useful for researchers in quantitative methods. … the book presents the standard methods of SEM in a form that makes them interesting to students and researchers with interests in the philosophical treatment of causality using SEM. … a very useful textbook for graduate students. It stands out for its rigorous treatment of SEM as a whole and for a particularly useful philosophical treatment of causality. … Stanley Mulaik’s book is one of the most useful ones with which to start a journey in this field.
—Spiridon Penev, Australian & New Zealand Journal of Statistics, 2011

The book benefits very substantially from the author’s mixed background in multivariate analysis, psychometrics, and philosophy of science—a background which is ideally suited to the eclectic issues raised by considerations of causality. I am sure the volume will prove to be a very useful contribution to the literature, an excellent text for someone intending to research in this area, and a useful reference source for those already doing so.
—David J. Hand, International Statistical Review (2011), 79

Editorial Reviews

"…an accessible yet rigorous treatment of the subject and is likely to be appealing to a wide statistical audience. … I enjoyed reading this book and suspect others will too. I would recommend this book for students and researchers [who] are familiar with standard applied statistics and causal inference but are looking for an introduction to structural equation modeling."
—Eric Laber, Journal of the American Statistical Association, September 2013

"The book is written by one of the most prominent researchers in the field of structural equation models (SEM). … It is primarily a useful textbook for graduate students but could also be very useful for researchers in quantitative methods. … the book presents the standard methods of SEM in a form that makes them interesting to students and researchers with interests in the philosophical treatment of causality using SEM. … a very useful textbook for graduate students. It stands out for its rigorous treatment of SEM as a whole and for a particularly useful philosophical treatment of causality. … Stanley Mulaik’s book is one of the most useful ones with which to start a journey in this field."
—Spiridon Penev, Australian & New Zealand Journal of Statistics, 2011

"The book benefits very substantially from the author’s mixed background in multivariate analysis, psychometrics, and philosophy of science—a background which is ideally suited to the eclectic issues raised by considerations of causality. I am sure the volume will prove to be a very useful contribution to the literature, an excellent text for someone intending to research in this area, and a useful reference source for those already doing so."
—David J. Hand, International Statistical Review (2011), 79

From the Publisher