1. Introduction. 2. Data Entry and Data Editing Issues. 3. Correlation. 4. SEM Basics. 5. Model Fit. 6. Regression Models. 7. Path Models. 8. Confirmatory Factor Models. 9. Developing Structural Equation Models: Part I. 10. Developing Structural Equation Models: Part II. 11. Reporting SEM Research: Guidelines and Recommendations. 12. Model Validation. 13. Multiple Sample, Multiple Group, and Structured Means Models. 14. Second Order, Dynamic, and Multi Trait Multi Method Models. 15. Multiple Indicator Multiple Indicator Cause, Mixture, and Multi-Level Models. 16. Interaction, Latent Growth, and Monte Carlo Methods. 17. Matrix Approach to Structural Equation Modeling.
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.a His research interests include modeling interaction in SEM, robust statistics, measurement model issues related to estimation, and reliability.aa 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.a
"The authors' considerable experience as modelers and teachers really shines throughout this edition, as reflected in the accessibility and coverage of the writing, the extensive practical software examples, and the useful troubleshooting and reporting tips." - Gregory R. Hancock, University of Maryland, USA "The authors guide us through SEM basics to more advanced techniques in an easily comprehensible style. As such, it is a great resource for both novice and veteran users of SEM." - Maria Regina Reyes, Yale University, USA "Their step-by-step approach ! makes the "how-to" extremely clear! The reader comes away not only knowing the logistics of how to run the models but also the conceptual of when to run them and how to interpret the findings.a Their coverage of assumptions, data cleaning and screening, and common SEM errors is extremely refreshing for those who work with real, messy data. This is a much anticipated edition to the already classical text." - Debbie Hahs-Vaughn, University of Central Florida, USA "There are a number of features that set this book apart ... it covers a variety of applications ... from simple regression models to highly complex analyses. ...[and] it takes a non-mathematical approach which makes [it] less intimidating... students have found it to be quite readable and friendly ... I have continued to use it because it is the most comprehensive and helpful to students." - Philip Smith, Dept. of Ed Leadership, Counseling, & Special Education, Augusta State University, USA
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