Mixed effects binary logit models are an important family of regression models for the analysis of panel data when the dependent variable is dichotomous. The model may include fixed as well as random (unit-specific) effects. This book reviews several common Markov chain Monte Carlo (MCMC) methods for estimating the parameters of mixed effects binary logit models within a Bayesian framework. In addition, the book presents some newly developed data augmentation methods for estimating logit models (auxiliary mixture sampling / data augmentation and frequentist estimation), which are adapted to...