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Bayesian Statistics for the Social Sciences

Bayesian Statistics for the Social Sciences

ISBN 9781462516513
Edition 1
Publication Date
Purchase Type Buy New
Publisher The Guilford Press
Author(s)
Overview
Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an ‘‘evidence-based'‘ framework for the practice of Bayesian statistics. Useful features for teaching or self-study:*Includes worked-through, substantive examples, using large-scale educational and social science databases.*Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs.*Shows readers how to carefully warrant priors on the basis of empirical data.*Companion website features data and code for the book's examples, plus other resources.KEY POINTS*Translational book: a reference and text that makes developments in Bayesian methods accessible to social science researchers.*Helps social scientists better examine the predictive quality of proposed models by incorporating prior knowledge*Includes worked-through examples from large, publicly accessible datasets, which are built on throughout the book.*Uses open-source R software programs, such as MCMCpack and JAGS; readers do not have to master the R language.*Online supplement: companion website provides R programs used in the book, plus data and code for the book's examples.AUDIENCEBehavioral and social science researchers; instructors and graduate students in psychology, education, sociology, management, and public health.COURSE USEWill serve as a core book for courses on Bayesian or advanced quantitative techniques.
Overview
Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an ‘‘evidence-based'‘ framework for the practice of Bayesian statistics. Useful features for teaching or self-study:*Includes worked-through, substantive examples, using large-scale educational and social science databases.*Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs.*Shows readers how to carefully warrant priors on the basis of empirical data.*Companion website features data and code for the book's examples, plus other resources.KEY POINTS*Translational book: a reference and text that makes developments in Bayesian methods accessible to social science researchers.*Helps social scientists better examine the predictive quality of proposed models by incorporating prior knowledge*Includes worked-through examples from large, publicly accessible datasets, which are built on throughout the book.*Uses open-source R software programs, such as MCMCpack and JAGS; readers do not have to master the R language.*Online supplement: companion website provides R programs used in the book, plus data and code for the book's examples.AUDIENCEBehavioral and social science researchers; instructors and graduate students in psychology, education, sociology, management, and public health.COURSE USEWill serve as a core book for courses on Bayesian or advanced quantitative techniques.
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