The second edition of this practical book equips social science researchers to apply the latest Bayesian methodologies to their data analysis problems. Clearly explaining frequentist and epistemic probability and prior distributions, the text covers Hamiltonian Monte Carlo, Bayesian linear regression and generalized linear models, model evaluation and comparison, multilevel modeling, models for continuous and categorical latent variables, missing data, and more. Concepts are illustrated with worked-through examples from large-scale educational and social science databases. Annotated RStan code appears in screened boxes; the companion website (www.guilfird.com/kaplan-materials) provides data sets and code for the books examples. New to This Edition: Chapters on Bayesian variable selection and sparsity, model uncertainty and model averaging, and Bayesian workflow for statistical modeling. Utilizes the R interface to Stan-faster and more stable than previously available Bayesian software. Coverage of Hamiltonian Monte Carlo; Cromwell's rule; Jeffreys' prior; the LKJ prior for correlation matrices; model evaluation and model comparison, with a critique of the Bayesian information criterion; variational Bayes as an alternative to Markov chain Monte Carlo sampling; and other new topics.