Bayesian Structural Equation Modeling

Sarah Depaoli

Hardcovere-bookprint + e-book
Hardcover
August 16, 2021
ISBN 9781462547746
Price: $75.00 $56.25
518 Pages
Size: 7" x 10"
pre-order
e-book
August 16, 2021
PDF ?
Price: $75.00 $56.25
518 Pages
pre-order
print + e-book
Hardcover + e-Book (PDF) ?
Price: $150.00 $82.50
518 Pages
pre-order

“Researchers interested in applying Bayesian SEM in the social sciences will benefit from reading this book or taking a course based on it. Each chapter is well organized; the introduction sections are particularly useful. All methods are illustrated by code, which is an important step toward implementing the methods and applying them to real problems.”

—Peng Ding, PhD, Department of Statistics, University of California, Berkeley


“This book is a 'must read' for anyone who wants to do or review Bayesian SEM. It is structured well for the advanced graduate student and moderately versed researcher. The chapters are highly readable, and I really appreciate the annotated bibliography of select resources, which will be a great help to students and faculty.”

—Michael D. Toland, PhD, Executive Director, The Herb Innovation Center, Judith Herb College of Education, University of Toledo


“The structure of each chapter is extremely well thought-out and facilitates understanding. A brief introduction to each topic is followed by an in-depth discussion, an example, and hypothetical results and discussion. The section about how to write up findings for each SEM analysis will be extremely helpful to readers; this is something that instructors are typically left to try to come up with on their own. I would absolutely consider using this book for a class on Bayesian SEM—or a lecture on the topic in a broader SEM course—as well as for my own professional use as a reference guide.”

—Katerina Marcoulides, PhD, Department of Psychology, University of Minnesota Twin Cities


“Depaoli has created a book that will quickly have a positive impact on researchers and students looking to expand their analytic capabilities. The text's design and writing style will engage readers with different levels of familiarity with Bayesian analysis and SEM. Instructors can flexibly change the level and amount of technical and mathematical information for different courses. I will add this text to my course to replace the hodgepodge of documents, website links, and articles needed for comprehension and usage of Bayesian SEM.”

—James B. Schreiber, PhD, School of Nursing, Duquesne University