PERHAPS A GIFT VOUCHER FOR MUM?: MOTHER'S DAY

Close Notification

Your cart does not contain any items

Bayesian Multilevel Models for Repeated Measures Data

A Conceptual and Practical Introduction in R

Santiago Barreda Noah Silbert

$263

Hardback

Not in-store but you can order this
How long will it take?

QTY:

English
Routledge
18 May 2023
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.

By:   ,
Imprint:   Routledge
Country of Publication:   United Kingdom
Dimensions:   Height: 246mm,  Width: 174mm, 
Weight:   1.300kg
ISBN:   9781032259628
ISBN 10:   1032259620
Pages:   460
Publication Date:  
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

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.

See Also