logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Bayesian Structural Equation Modeling 1st Edition Sarah Depaoli

  • SKU: BELL-33789706
Bayesian Structural Equation Modeling 1st Edition Sarah Depaoli
$ 31.00 $ 45.00 (-31%)

4.3

58 reviews

Bayesian Structural Equation Modeling 1st Edition Sarah Depaoli instant download after payment.

Publisher: Guilford Press
File Extension: PDF
File size: 11.96 MB
Pages: 518
Author: Sarah Depaoli
ISBN: 9781462547746, 1462547745
Language: English
Year: 2021
Edition: 1

Product desciption

Bayesian Structural Equation Modeling 1st Edition Sarah Depaoli by Sarah Depaoli 9781462547746, 1462547745 instant download after payment.

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 excerpts of annotated code in both Mplus and R. The companion website supplies datasets, code, and output for all of the book’s examples.

Related Products