The second edition of this practical book equips social science researchers to apply the latest Bayesian methodologies to their data analysis problems. It includes new chapters on model uncertainty, Bayesian variable selection and sparsity, and Bayesian workflow for statistical modeling. Clearly explaining frequentist and epistemic probability and prior distributions, the second edition emphasizes use of the open-source RStan software package. 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 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. 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. 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. *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. *Chapters on Bayesian variable selection and sparsity, model uncertainty and model averaging, and Bayesian workflow for statistical modeling.

Table of Contents:
I. Foundations 1. Probability Concepts and Bayes' Theorem 1.1 Relevant Probability Axioms 1.1.1 The Kolmogorov Axioms of Probability 1.1.2 The Rényi Axioms of Probability 1.2 Frequentist Probability 1.3 Epistemic Probability 1.3.1 Coherence and the Dutch Book 1.3.2 Calibrating Epistemic Probability Assessment 1.4 Bayes' Theorem 1.4.1 The Monty Hall Problem 1.5 Summary 2. Statistical Elements of Bayes' Theorem 2.1 Bayes' Theorem Revisited 2.2. Hierarchical Models and Pooling 2.3 The Assumption of Exchangeability 2.4 The Prior Distribution 2.4.1 Non-informative Priors 2.4.2 Jeffreys' Prior 2.4.3 Weakly Informative Priors 2.4.4 Informative Priors 2.4.5 An Aside: Cromwell's Rule 2.5 Likelihood 2.5.1 The Law of Likelihood 2.6 The Posterior Distribution 2.7 The Bayesian Central Limit Theorem and Bayesian Shrinkage 2.8 Summary 3. Common Probability Distributions and Their Priors 3.1 The Gaussian Distribution 3.1.1 Mean Unknown, Variance Known: The Gaussian Prior 3.1.2 The Uniform Distribution as a Non-informative Prior 3.1.3 Mean Known, Variance Unknown: The Inverse-Gamma Prior 3.1.4 Mean Known, Variance Unknown: The Half-Cauchy Prior 3.1.5 Jeffreys' Prior for the Gaussian Distribution 3.2 The Poisson Distribution 3.2.1 The Gamma Prior 3.2.2 Jeffreys' Prior for the Poisson Distribution 3.3 The Binomial Distribution 3.3.1 The Beta Prior 3.3.2 Jeffreys' Prior for the Binomial Distribution 3.4 The Multinomial Distribution 3.4.1 The Dirichlet Prior 3.4.2 Jeffreys' Prior for the Multinomial Distribution 3.5 The Inverse-Wishart Distribution 3.6 The LKJ Prior for Correlation Matrices 3.7 Summary 4. Obtaining and Summarizing the Posterior Distribution 4.1 Basic Ideas of Markov Chain Monte Carlo Sampling 4.2 The Random Walk Metropolis–Hastings Algorithm 4.3 The Gibbs Sampler 4.4 Hamiltonian Monte Carlo 4.4.1 No-U-Turn (NUTS) Sampler 4.5 Convergence Diagnostics 4.5.1 Trace Plots 4.5.2 Posterior Density Plots 4.5.3 Auto-Correction Plots 4.5.4 Effective Sample Size 4.5.5 Potential Scale Reduction Factor 4.5.6 Possible Error Messages When Using HMC/NUTS 4.6 Summarizing the Posterior Distribution 4.6.1 Point Estimates of the Posterior Distribution 4.6.2 Interval Summaries of the Posterior Distribution 4.7 Introduction to Stan and Example 4.8 An Alternative Algorithm: Variational Bayes 4.8.1 Evidence Lower Bound (ELBO) 4.8.2 Variational Bayes Diagnostics 4.9 Summary II. Bayesian Model Building 5. Bayesian Linear and Generalized Models 5.1 The Bayesian Linear Regression Model 5.1.1 Non-informative Priors in the Linear Regression Model 5.2 Bayesian Generalized Linear Models 5.2.1 The Link Function 5.3 Bayesian Logistic Regression 5.4 Bayesian Multinomial Regression 5.5 Bayesian Poisson Regression 5.6 Bayesian Negative Binomial Regression 5.7 Summary 6. Model Evaluation and Comparison 6.1 The Classical Approach to Hypothesis Testing and Its Limitations 6.2 Model Assessment 6.2.1 Prior Predictive Checking 6.2.2 Posterior Predictive Checking 6.3 Model Comparison 6.3.1 Bayes Factors 6.3.2 The Deviance Information Criterion (DIC) 6.3.3 Widely Applicable Information Criterion (WAIC) 6.3.4 Leave-One-Out Cross-Validation 6.3.5 A Comparison of WAIC and LOO 6.4 Summary 7. Bayesian Multilevel Modeling 7.1 Revisiting Exchangeability 7.2 Bayesian Random Effects Analysis of Variance 7.3 Bayesian Intercepts as Outcomes Model 7.4 Bayesian Intercepts and Slopes as Outcomes Model 7.5 Summary 8. Bayesian Latent Variable Modeling 8.1 Bayesian Estimation for the CFA 8.1.1 Priors for CFA Model Parameters 8.2 Bayesian Latent Class Analysis 8.2.1 The Problem of Label-Switching and a Possible Solution 8.2.2 Comparison of VB to the EM Algorithm 8.3 Summary III. Advanced Topics and Methods 9. Missing Data From a Bayesian Perspective 9.1 A Nomenclature for Missing Data 9.2 Ad Hoc Deletion Methods for Handling Missing Data 9.2.1 Listwise Deletion 9.2.2 Pairwise Deletion 9.3 Single Imputation Methods 9.3.1 Mean Imputation 9.3.2 Regression Imputation 9.3.3 Stochastic Regression Imputation 9.3.4 Hot Deck Imputation 9.3.5 Predictive Mean Matching 9.4 Bayesian Methods for Multiple Imputation 9.4.1 Data Augmentation 9.4.2 Chained Equations 9.4.3 EM Bootstrap: A Hybrid Bayesian/Frequentist Methods 9.4.4 Bayesian Bootstrap Predictive Mean Matching 9.4.5 Accounting for Imputation Model Uncertainty 9.5 Summary 10. Bayesian Variable Selection and Sparsity 10.1 Introduction 10.2 The Ridge Prior 10.3 The Lasso Prior 10.4 The Horseshoe Prior 10.5 Regularized Horseshoe Prior 10.6 Comparison of Regularization Methods 10.6.1 An Aside: The Spike-and-Slab Prior 10.7 Summary 11. Model Uncertainty 11.1 Introduction 11.2 Elements of Predictive Modeling 11.2.1 Fixing Notation and Concepts 11.2.2 Utility Functions for Evaluating Predictions 11.3 Bayesian Model Averaging 11.3.1 Statistical Specification of BMA 11.3.2 Computational Considerations 11.3.3 Markov Chain Monte Carlo Model Composition 11.3.4 Parameter and Model Priors 11.3.5 Evaluating BMA Results: Revisiting Scoring Rules 11.4 True Models, Belief Models, and M-Frameworks 11.4.1 Model Averaging in the M-Closed Framework 11.4.2 Model Averaging in the M-Complete Framework 11.4.3 Model Averaging in the M-Open Framework 11.5 Bayesian Stacking 11.5.1 Choice of Stacking Weights 11.6 Summary 12. Closing Thoughts 12.1 A Bayesian Workflow for the Social Sciences 12.2 Summarizing the Bayesian Advantage 12.2.1 Coherence 12.2.2 Conditioning on Observed Data 12.2.3 Quantifying Evidence 12.2.4 Validity 12.2.5 Flexibility in Handling Complex Data Structures 12.2.6 Formally Quantifying Uncertainty List of Abbreviations and Acronyms References Author Index Subject Index