Bayesian Statistics for the Social Sciences by David Kaplan - PDF free download eBook

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  • Published: Dec 09, 2015
  • Reviews: 391

Brief introduction:

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...

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Details of Bayesian Statistics for the Social Sciences

ISBN
9781462516513
Publisher
Guilford Publications, Inc.
Publication date
Age range
18+ Years
Book language
ENG
Pages
318
Format
PDF, CHM, FB2, FB3
Quality
High quality scanned pages
Dimensions
6.10 (w) x 9.30 (h) x 1.00 (d)
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Some brief overview of this book

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 books examples, plus other resources.

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A few words about book author

David Kaplan, PhD, is Professor of Quantitative Methods in the Department of Educational Psychology at the University of Wisconsin–Madison and holds affiliate appointments in the Department of Population Health Sciences and the Center for Demography and Ecology. Dr. Kaplan’s program of research focuses on the development of Bayesian statistical methods for education research. His work on these topics is directed toward application to quasi-experimental and large-scale cross-sectional and longining 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 books examples, plus other resources.

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