Raykov (measurement and quantitative methods, Michigan State U.) and Marcoulides (information systems and decision sciences, California State U., Fullerton) assume their readers, including students, have limited or no previous exposure to structural equation modeling (SEM) but want to apply it to their work in applied social, behavior and health sciences. Focusing on the mathematical formulas as concepts or illustrations rather than computational devices, they include substantial references to EQS, LISREL and Mplus to describe how to set up input files to fit the most commonly used types of SEM in this programs. They cover the fundamentals of SEM, the basics of the programs, path analysis, confirmatory factor analysis, structural regression models and latent change analysis. This edition includes technological updates but maintains the same commitment to providing fundamental information independent of particular programs as the original. Annotation ©2006 Book News, Inc., Portland, OR (booknews.com)
In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one. Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner’s guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software.
Highlights of the second edition include:
* review of latent change (growth) analysis models at an introductory level;
*coverage of the popular Mplus program;
*updated examples of LISREL and EQS; and
*a CD that contains all of the text’s LISREL, EQS, and Mplus examples.
A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.
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