Descripción del título
Bayesian structural equatio...
This book is meant as a guide for implementing Bayesian methods for latent variable models. I have included thorough examples in each chapter, highlighting problems that can arise during estimation, potential solutions, and guides for how to write up findings for a journal article. This book is structured into 12 main chapters, beginning with introductory chapters comprising Part I. Part II is comprised of Chapters 3-5. Each of these chapters deals with various models and techniques related to measurement models within SEM. Part III contains Chapters 6-7, on extending the structural model. Part IV contains Chapters 8-10, on longitudinal and mixture models. Finally, Part IV contains chapters that discuss special topics"
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. "
Monografía
monografia Rebiun33880220 https://catalogo.rebiun.org/rebiun/record/Rebiun33880220 211012s2021 nyu b 000 0 eng d 9781462547746 cloth) 1462547745 UIB (446351) DKDLA dan DKDLA OCLCF OCLCO OCLCQ Depaoli, Sarah Bayesian structural equation modeling Sarah Depaoli New York, NY The Guilford Press 2021 New York, NY New York, NY The Guilford Press xxvi, 520 s. xxvi, 520 s. Text txt rdacontent unmediated n rdamedia volume nc rdacarrier Methodology in the social sciences Includes bibliographical references and index This book is meant as a guide for implementing Bayesian methods for latent variable models. I have included thorough examples in each chapter, highlighting problems that can arise during estimation, potential solutions, and guides for how to write up findings for a journal article. This book is structured into 12 main chapters, beginning with introductory chapters comprising Part I. Part II is comprised of Chapters 3-5. Each of these chapters deals with various models and techniques related to measurement models within SEM. Part III contains Chapters 6-7, on extending the structural model. Part IV contains Chapters 8-10, on longitudinal and mixture models. Finally, Part IV contains chapters that discuss special topics" 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. " Bayesian statistical decision theory Social sciences- Statistical methods Théorie de la décision bayésienne Sciences sociales- Méthodes statistiques Bayesian statistical decision theory. Social sciences- Statistical methods. Methodology in the social sciences