A Beginner's Guide to Structural Equation Modeling
Published April 21st 2010 by Routledge – 536 pages
This best-seller introduces readers to structural equation modeling (SEM) so they can conduct their own analysis and critique related research. Noted for its accessible, applied approach, chapters cover basic concepts and practices and computer input/output from the free student version of Lisrel 8.8 in the examples. Each chapter features an outline, key concepts, a summary, numerous examples from a variety of disciplines, tables, and figures, including path diagrams, to assist with conceptual understanding.
The book first reviews the basics of SEM, data entry/editing, and correlation. Next the authors highlight the basic steps of SEM: model specification, identification, estimation, testing, and modification, followed by issues related to model fit and power and sample size. Chapters 6 through 10 follow the steps of modeling using regression, path, confirmatory factor, and structural equation models. Next readers find a chapter on reporting SEM research including a checklist to guide decision-making, followed by one on model validation. Chapters 13 through 16 provide examples of various SEM model applications. The book concludes with the matrix approach to SEM using examples from previous chapters.
Highlights of the new edition include:
Designed for introductory graduate level courses in structural equation modeling or factor analysis taught in psychology, education, business, and the social and healthcare sciences, this practical book also appeals to researchers in these disciplines. An understanding of correlation is assumed. To access the website visit the book page or the Textbook Resource page at http://www.psypress.com/textbook-resources/ for more details.
"From the first edition this book has been the leading book on this topic, providing an authoritative and systematic treatment of SEM for both researchers and practitioners. [It is] well organised and clearly written [and] can be recommended as a textbook to teach a full course in SEM. [A] good mixture of theory and practical applications … graduate and research students will definitely enjoy reading this book [and] practitioners may find the book useful. I would also recommend it for library purchase." - Kuldeep Kumar, Bond University, Gold Coast, in the Journal of the Royal Statistical Society
"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. 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
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. 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.