Basic Principles of Structural Equation Modeling An Introduction
During the last two decades, structural equation modeling (SEM) has emerged as a powerful multivariate data analysis tool in social science research settings, especially in the fields of sociology, psychology, and education. Although its roots can be trac
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George Casella Stephen Fienberg
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Ingram Olkin
Springer Texts in Statistics Alfred: Elements of Statistics for the Life and Social Sciences Berger: An Introduction to Probability and Stochastic Processes Blom: Probability and Statistics: Theory and Applications Brockwell and Davis: An Introduction to Times Series and Forecasting Chow and Teicher: Probability Theory: Independence, Interchangeability, Martingales, Third Edition Christensen: Plane Answers to Complex Questions: The Theory of Linear Models, Second Edition Christensen: Linear Models for Multivariate, Time Series, and Spatial Data Christensen: Log-Linear Models and Logistic Regression, Second Edition Creighton: A First Course in Probability Models and Statistical Inference Dean and Voss: Design and Analysis of Experiments du Toit, Steyn, and Stumpf" Graphical Exploratory Data Analysis Edwards: Introduction to Graphical Modelling Finkelstein and Levin: Statistics for Lawyers Flury: A First Course in Multivariate Statistics Jobson: Applied Multivariate Data Analysis, Volume I: Regression and Experimental Design Jobson: Applied Multivariate Data Analysis, Volume II: Categorical and Multivariate Methods Kalbfleisch: Probability and Statistical Inference, Volume I: Probability, Second Edition Kalbfleisch: Probability and Statistical Inference, Volume II: Statistical Inference, Second Edition Karr: Probability KeyJitz: Applied Mathematical Demography, Second Edition Kiefer: Introduction to Statistical Inference Kokoska and Nevison: Statistical Tables and Formulae Lehmann: Elements of Large-Sample Theory Lehmann: Testing Statistical Hypotheses, Second Edition Lehmann and Casella: Theory of Point Estimation, Second Edition Lindman: Analysis of Variance in Experimental Design Lindsey: Applying Generalized Linear Models Madansky: Prescriptions for Working Statisticians McPherson: Statistics in Scientific Investigation: Its Basis, Application, and Interpretation Mueller: Basic Principles of Structural Equation Modeling Nguyen and Rogers: Fundamentals of Mathematical Statistics: Volume I: Probability for Statistics Nguyen and Rogers: Fundamentals of Mathematical Statistics: Volume II: Statistical Inference
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Ralph O. Mueller
Basic Principles of Structural Equation Modeling An Introduction to LISREL and EQS
With 25 Illustrations
Springer
Ralph O. Mueller, PhD Department of Educational Leadership Graduate School of Education and Human Development The George Washington University Washington, DC 20052 USA [email protected] edu
Editorial Board George Casella Biometrics Unit Cornell University Ithaca. NY 14853-7801 USA
Stephen Fienberg Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213-3890 USA
Ingram Olkin Department of Statistics Stanford University Stanford, CA 94305 USA
On the cover: The artwork is based on an illustration of the basic matrices for structural equation
models. Library of Congress Cataloging-in-Public
