This book provides the reader with a review of correlation and covariance among variables, followed by multiple regression and path analysis techniques to better understand the building blocks of structural equation modelling. 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. Examples are included throughout to make the concepts clear to the reader. The structural equation modelling examples are presented using either EQS5.0 or LISREL8-SIMPLIS programming language, both of which have an easy-to-use set of commands to specify measurement and strucural models. No complicated programming is required, nor does the reader need an advanced understanding of statistics of matrix algebra. A goal in writing this volume was to focus conceptually on the steps one takes in analyzing theoretical models. These steps encompass: 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 based on the distributional assumptions of variables; testing the model and interpreting fit indices; and finally respecifying a model based on suggested modification indices, which involves adding or dropping paths in the model to obtain a better model fit. The resources and references provided in this book should equip faculty, students and researchers to enhance their working knowledge of structural equation modelling. Not intended as an in-depth presentation of statistics or factor analysis, this text focuses on the basic ideas and principles behind structural equation modelling. Assuming that the reader has a basic understanding of correlation, the authors have built upon this understanding to present these basic ideas and principles.
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Randall E. Schumacker is Professor of Educational Research at The University of Alabama where he teaches courses in structural equation modeling. He received his Ph.D. in Educational Psychology from Southern Illinois University. A Past-President of the Southwest Educational Research Association and Emeritus Editor of Structural Equation Modeling, Dr. Schumacker has also served on the editorial boards of numerous journals. His research interests include modeling interaction in SEM, robust statistics, measurement model issues related to estimation, and reliability.
Richard G. Lomax is a Professor in the School of Educational Policy and Leadership at The Ohio State University where he teaches courses in structural equation modeling. He received his Ph.D. in Educational Research Methodology from the University of Pittsburgh. He has served on the editorial boards of numerous journals. His research focuses on models of literacy acquisition, multivariate statistics, and assessment.
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