Bayesian Multilevel Models for Repeated Measures Data - A Conceptual and Practical Introduction in R (Hardcover)

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, 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.

General

Imprint:	Taylor & Francis
Country of origin:	United Kingdom
Release date:	May 2023
First published:	2023
Authors:	Santiago Barreda • Noah Silbert
Dimensions:	246 x 174mm (L x W)
Format:	Hardcover
Pages:	440
ISBN-13:	978-1-03-225962-8
Categories:	Books > Reference & Interdisciplinary > Communication studies > Research methods Books > Science & Mathematics > Mathematics > Probability & statistics Books > Social sciences > Psychology > Philosophy & theory of psychology > General Books > Social sciences > Psychology > Psychological methodology > General Books > Business & Economics > Economics > Econometrics > Economic statistics Promotions
LSN:	1-03-225962-0
Barcode:	9781032259628

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