Bayesian statistics for the social sciences

By: Kaplan, David
Material type: TextTextSeries: Methodology in the Social SciencesPublisher: New York The Guilford Press 2014Description: xviii, 318 p.ISBN: 9781462516513Subject(s): Social sciences - Statistical methods | Bayesian statistical decision theoryDDC classification: 519.542 Summary: 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, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth). 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. (http://www.guilford.com/books/Bayesian-Statistics-for-the-Social-Sciences/David-Kaplan/9781462516513)
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Books Vikram Sarabhai Library
Slot 1676 (2 Floor, East Wing) Non-fiction 519.542 K2B2 (Browse shelf) Available 188573

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, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth).
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. (http://www.guilford.com/books/Bayesian-Statistics-for-the-Social-Sciences/David-Kaplan/9781462516513)

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