Bayesian Multilevel Models for Repeated Measures Data: A Conceptual and Practical Introduction in R

by Santiago Barreda, Noah Silbert

Overview

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.

Product Details

ISBN-13:	9781032259628
Publisher:	Taylor & Francis
Publication date:	05/18/2023
Pages:	484
Product dimensions:	6.88(w) x 9.69(h) x (d)

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.

Table of Contents

Preface

Acknowledgments

1. Introduction: Experiments and Variables

2. Probabilities, Likelihood, and Inference

3. Fitting Bayesian Regression Models with brms

4. Inspecting a ‘Single Group’ of Observations using a Bayesian Multilevel Model

5. Comparing Two Groups of Observations: Factors and Contrasts

6. Variation in Parameters (‘Random Effects’) and Model Comparison

7. Comparing Many Groups, Interactions, and Posterior Predictive Checks

8. Varying Variances, More about Priors, and Prior Predictive Checks

9. Quantitative Predictors and their Interactions with Factors

10. Logistic Regression and Signal Detection Theory Models

11. Multiple Quantitative Predictors, Dealing with Large Models, and Bayesian ANOVA

12. Multinomial and Ordinal Regression

13. Writing up Experiments: An investigation of the Perception of Apparent Speaker Characteristics from Speech Acoustics