Bayesian Structural Equation Modeling (Methodology in the Social Sciences)

Sarah Depaoli, PhD, is Associate Professor of Quantitative Methods, Measurement, and Statistics in the Department of Psychological Sciences at the University of California, Merced, where she teaches undergraduate statistics and a variety of graduate courses in quantitative methods. Her research interests include examining different facets of Bayesian estimation for latent variable, growth, and finite mixture models. She has a continued interest in the influence of prior distributions and robustness of results under different prior specifications, as well as issues tied to latent class separation. Her recent research has focused on using Bayesian semi- and non-parametric methods for obtaining proper class enumeration and assignment, examining parameterization issues within Bayesian SEM, and studying the impact of priors on longitudinal models.

Title: Bayesian Structural Equation Modeling (Methodology in the Social Sciences)

Author: Depaoli, Sarah

ISBN: 9781462547746

Binding:

Publisher: Guilford Publications

Publication Date: 2021-10-26

Number of Pages: 521

Weight: 1.1083 kg

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

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-