The second edition of this practical book equips Social science researchers to apply the latest Bayesian methodologies to their data analysis problems. *Chapters on Bayesian variable selection and sparsity, model uncertainty and model averaging, and Bayesian workflow for statistical modeling.. *Coverage of Hamiltonian MC
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 (MCMC) sampling; and other new topics.
New to This Edition *Utilizes the R interface to Stan--faster and more stable than previously available Bayesian software--for most of the applications discussed.
Annotated RStan code appears in screened boxes; the companion website ( www.guilford.com/kaplan-materials ) provides data sets and code for the book\'s examples.
Concepts are fully illustrated with worked-through examples from large-scale educational and Social science databases, such as the Program for International Student Assessment and the Early Childhood Longitudinal Study.
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.
Clearly explaining frequentist and epistemic probability and prior distributions, the second edition emphasizes use of the open-source RStan software package.
It includes new chapters on model uncertainty, Bayesian variable selection and sparsity, and Bayesian workflow for statistical modeling.
The second edition of this practical book equips Social science researchers to apply the latest Bayesian methodologies to their data analysis problems
