Bayesian Structural Equation Modeling, Hardcover/Sarah Depaoli

Descriere YEO:

Bayesian Structural Equation Modeling, Hardcover/Sarah - Disponibil la elefant.ro

Pe YEO găsești Bayesian Structural Equation Modeling, Hardcover/Sarah de la Guilford Publications, în categoria Foreign Books.

Indiferent de nevoile tale, Bayesian Structural Equation Modeling, Hardcover/Sarah Depaoli din categoria Foreign Books îți poate aduce un echilibru perfect între calitate și preț, cu avantaje practice și moderne.

Preț: 540.99 Lei

Caracteristicile produsului Bayesian Structural Equation Modeling, Hardcover/Sarah

• Brand: Guilford Publications
• Categoria: Foreign Books
• Magazin: elefant.ro
• Ultima actualizare: 21-12-2024 01:38:29

Comandă Bayesian Structural Equation Modeling, Hardcover/Sarah Online, Simplu și Rapid

Prin intermediul platformei YEO, poți comanda Bayesian Structural Equation Modeling, Hardcover/Sarah de la elefant.ro rapid și în siguranță. Bucură-te de o experiență de cumpărături online optimizată și descoperă cele mai bune oferte actualizate constant.

Descriere magazin:
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 M plus and R. The companion website ( www.guilford.com/depaoli-materials ) supplies datasets; annotated code for implementation in both M plus and R, so that users can work within their preferred platform; and output for all of the book\'s examples. About author(s): 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.