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Bayesian Multilevel Models for Repeated Measures Data

- A Conceptual and Practical Introduction in R
Af: Noah Silbert, Santiago Barreda Engelsk Paperback

Bayesian Multilevel Models for Repeated Measures Data

- A Conceptual and Practical Introduction in R
Af: Noah Silbert, Santiago Barreda Engelsk Paperback
Tjek vores konkurrenters priser

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.

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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.

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Produktdetaljer
Sprog: Engelsk
Sider: 460
ISBN-13: 9781032259635
Indbinding: Paperback
Udgave:
ISBN-10: 1032259639
Kategori: Forskningsmetoder
Udg. Dato: 18 maj 2023
Længde: 29mm
Bredde: 246mm
Højde: 174mm
Forlag: Taylor & Francis Ltd
Oplagsdato: 18 maj 2023
Forfatter(e): Noah Silbert, Santiago Barreda
Forfatter(e) Noah Silbert, Santiago Barreda

Kategori Forskningsmetoder

ISBN-13 9781032259635

Sprog Engelsk

Indbinding Paperback

Sider 460

Udgave

Længde 29mm

Bredde 246mm

Højde 174mm

Udg. Dato 18 maj 2023

Oplagsdato 18 maj 2023

Forlag Taylor & Francis Ltd

Kategori sammenhænge