Bayesian multilevel models in R: A conceptual and practical introduction for linguists
This book is currently under development and it may contain small errors, inconsistencies, etc. It was used for a 10-week statistics class, but is being expanded and reorganized somewhat.
Repeated measures data is extremely common in linguistics, and the norm in many linguistic subfields (phonetics, variationist sociolinguistics, psycholinguistics, etc.). Bayesian multilevel models are perfectly suited for the analysis of repeated-measures data, and offer linguists flexibility and many exciting opportunities. Because of this, there is tremendous interest in using Bayesian multilevel models in linguistics despite the lack of available resources. This book is an introduction to the analysis of repeated-measures data using Bayesian multilevel regression models, specifically aimed at linguists with no background in statistics.
This book is intended for an introductory statistics class for senior undergraduate or graduate students, and for faculty members and other researchers to use as a self-study guide. This book is aimed at 1) students who are looking for a conceptual framework to help understand multilevel Bayesian models, but not necessarily looking for ‘too-much’ mathematical detail, and 2) researchers who are already experienced with frequentist modeling and are looking to ‘translate’ their skillset over to a Bayesian framework.
Each chapter of the book presents a different regression model design concept:
Chapter 1: Inspecting a single group of observations: Introduction to regression models
Chapter 2: Inspecting a ‘single group’ of observations using a Bayesian multilevel model
Chapter 3: Comparing two groups of observations: Factors and contrasts
Chapter 4: Comparing many groups: ANOVA and interactions
Chapter 5: Continuous predictors and their interactions with factors
Chapter 6: Random slopes and multiple random effects
If you have any comments or suggestions please let me know, and if you find any errors definitely let me know!