Bayesian Structural Equation Modeling by Depaoli, Sarah

Bayesian Structural Equation Modeling (Hardcover) (ISBN-13: 9781462547746)

Vendor: Sarah Depaoli
Product type: Books
Format: Hardcover
$80.00
$80.00
$80.00
Subtotal: $80.00
Bayesian Structural Equation Modeling by Depaoli, Sarah

Bayesian Structural Equation Modeling

$80.00
Bayesian Structural Equation Modeling by Depaoli, Sarah

Bayesian Structural Equation Modeling

$80.00
Format: Hardcover
Description
Description
x
This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book's examples.

Author: Sarah Depaoli
ISBN-10: 1462547745
ISBN-13: 9781462547746
Publisher: Guilford Publications
Language: English
Published: 08/16/2021
Pages: 521
Format: Hardcover
Weight: 2.40lbs
Size: 10.10h x 6.90w x 1.30d
By Sarah Depaoli
By Sarah Depaoli
x
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.

Recently Viewed Products