1st Edition
Bayesian Multilevel Models for Repeated Measures Data A Conceptual and Practical Introduction in R
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
Biography
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.
