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TEXTBOOKS
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HANDBOOKS
Intermediate/Advanced Statistics - Bayesian
A STUDENT'S GUIDE TO BAYESIAN STATISTICS
Ben Lambert, University of Oxford, U.K.
Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and
interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing
technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide
approachable instruction perfectly aimed at statistics and Bayesian newcomers. The book's logical structure introduces
and builds upon key concepts in a gradual way and slowly acclimatises students to using R, Stan, and JAGS software.
CONTENTS 1. How to best use this book / PART I: AN INTRODUCTION TO BAYESIAN INFERENCE / 2. The subjective worlds of Frequentist and
Bayesian statistics / 3. Probability - the nuts and bolts of Bayesian inference / PART II: UNDERSTANDING THE BAYESIAN FORMULA / 4. The posterior
- the goal of Bayesian inference / 5. Likelihoods / 6. Priors / 7. The devil's in the denominator / PART III: ANALYTIC BAYESIAN METHODS / 8. An
introduction to distributions for the mathematically-un-inclined / 9. Conjugate priors and their place in Bayesian analysis / 10. Evaluation of
model fit and hypothesis testing / 11. Making Bayesian analysis objective? / PART IV: A PRACTICAL GUIDE TO DOING REAL LIFE BAYESIAN ANALYSIS:
COMPUTATIONAL BAYES / 12. Leaving conjugates behind: Markov Chain Monte Carlo / 13. The Metropolis algorithm / 14. Gibbs sampling / 15.
Hamiltonian Monte Carlo / 16. Stan and JAGS / PART V: REGRESSION ANALYSIS AND HIERARCHICAL MODELS / 17. Hierarchical models / 18. Linear
regression models / 19. Generalised linear models
PAPERBACK ISBN: 978-1-4739-1636-4 • ©2019 • 520 PAGES •
online
resources
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Intermediate/Advanced Statistics - Propensity Score Analysis
ADVANCED QUANTITATIVE TECHNIQUES IN THE SOCIAL SCIENCES SERIES
PROPENSITY SCORE ANALYSIS: Statistical Methods and Applications SECOND EDITION
Shenyang Guo, Washington University in St. Louis • Mark W. Fraser, University of North Carolina at
Chapel Hill
Propensity Score Analysis provides readers with a systematic review of the origins, history, and statistical foundations
of PSA and illustrates how it can be used for solving evaluation and causal-inference problems. With a strong focus on
practical applications, the authors explore various strategies for employing PSA.
CONTENTS 1: Introduction / 2: Counterfactual Framework and Assumptions / 3: Conventional Methods for Data Balancing / 4: Sample Selection
and Related Models / 5: Propensity Score Matching and Related Models / 6: Propensity Score Subclassification / Chapter 7: Propensity Score
Weighting / 8: Matching Estimators / 9: Propensity Score Analysis with Nonparametric Regression / 10: Propensity Score Analysis of Categorical or
Continuous Treatments / 11: Selection Bias and Sensitivity Analysis / 12: Concluding Remarks
HARDCOVER ISBN: 978-1-4522-3500-4 • ©2015 • 448 PAGES •
online
resources
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PROPENSITY SCORE METHODS AND APPLICATIONS VOLUME 178
Haiyan Bai, University of Central Florida • M. H. Clark, University of Central Florida
PAPERBACK: $22.00 • ISBN: 978-1-5063-7805-3 • ©2019 • 112 PAGES
SEE ALSO THE QUANTIATIVE APPLICATIONS IN THE SOCIAL SCIENCES SECTION ON PAGE 61 FOR MORE INFORMATION ON THIS
TEXT.
Intermediate/Advanced Statistics - Regression/Linear Modeling
NEW! / ADVANCED QUANTITATIVE TECHNIQUES IN THE SOCIAL SCIENCES SERIES
SPATIAL REGRESSION MODELS FOR THE SOCIAL SCIENCES
Guangqing Chi, The Pennsylvania State University • Jun Zhu, University of Wisconsin, Madison
Focusing on the methods that are commonly used by social scientists, this text introduces the regression methods for
analyzing spatial data. The authors explain what each method is and when and how to apply it, connecting concepts to
social science research topics. Avoiding mathematical formulas and symbols as much as possible, the book introduces
the methods in an easy-to-follow manner, providing comprehensive coverage and using the same social science
example throughout to demonstrate the applications of each method and what the results can tell us.
HARDCOVER ISBN: 978-1-5443-0207-2 • MAY 2019 • 272 PAGES •
online
resources
