Bayesian Multilevel Models for Repeated Measures Data
Author:Santiago Barreda (Author), Noah Silbert (Author)
Publisher finelybook 出版社: Routledge
Publication Date 出版日期: 2023-05-18
Edition 版本: 1st
Language 语言: English
Print Length 页数: 484 pages
ISBN-10: 1032259639
ISBN-13: 9781032259635
Book Description
This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated measures data, the focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book.
In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many ‘random effects’. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write up their own analyses.
This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to ‘translate’ their skills with more traditional models to a Bayesian framework will benefit greatly from the lessons in this text.
About the Author
Santiago Barreda is a phonetician in the Linguistics Department at the University of California, Davis, USA, with a particular interest in speech perception.
Noah Silbert is a former Academic and is currently a practicing Stoic. His training and background are in phonetics, perceptual modeling, and statistics.
