• Bayesian Statistics for the Social Sciences
  • Bayesian Statistics for the Social Sciences
ISBN: 1462516513
EAN13: 9781462516513
Language: English
Release Date: Jul 23, 2014
Pages: 318
Dimensions: 0.94" H x 9.29" L x 6.14" W
Weight: 1.46 lbs.
Format: Hardcover
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Book 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 I covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part I 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.

User-Friendly Features
*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.

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