This book reviews correlation and covariance among variables, followed by multiple regression and path analysis techniques to better understand the building blocks of structural equation modeling. The concepts behind measurement models are introduced to illustrate how measurement error impacts statistical analyses, and structural models are presented that indicate how latent variable relationships can be established. The modeling examples are presented using either EQS 5.0 or LISREL8-SIMPLIS, both of which have an easy-to-use set of commands to specify measurement and structural models. The authors focus on the steps taken in analyzing theoretical models: * specifying a model based upon theory or prior research; * determining whether the model can be identified to have unique estimates for variables in the model; * selecting an appropriate estimation method; * testing the model and interpreting fit indices; and * respecifying a model based upon suggested modification indices, which involves adding or dropping paths in the model to obtain a better model fit. Not intended as an in-depth presentation of statistics or factor analysis, this text focuses on the basic ideas and principles behind structural equation modeling. It assumes a basic understanding of correlation.
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Randall E. Schumacker is a Professor of Education Research at the University of North Texas. He has served on the editorial boards of numerous journals and is Emeritus Editor of Structural Equation Modeling.
Richard G. Lomax is a Professor of Education and Applied Statistics at the University of Alabama. He has published numerous artciles and presented numerous papers on related topics.
to conduct an SEM analysis. Checklists are provided for each step, from model specification through validation. The accompanying CD provides all of the programs needed to do the analysis, with accompanying screen shots and directions in the book to allow the reader to self-teach the software application. This book would serve well as either a reference text or a required text for a course."
—Technometrics
"This book would be an invaluable resource for any student or researcher who wishes to begin exploring SEM, path analysis, or confirmatory factor analysis and who is willing to conscientiously treat this book as a self-taught course....a book which manages to be clear and understandable without spoon-feeding or talking down to the reader."
—Journal of Personality Assessment
"Before Schumacker and Lomax, I had a difficult time identifying the 'one source' that would provide a very straightforward introduction to SEM, enough detail about these modeling methods and current issues in the field, and enough of a base for a reader to take the next step and formally consider their own data in SEM...I am a great fan of this book."
—Abigail T. Panter, Ph.D.
University of North Carolina, Chapel Hill
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