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Bayesian statistics for the social s...

Kaplan, David, (1955-)

Bayesian statistics for the social sciences /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian statistics for the social sciences /David Kaplan.
Author:	Kaplan, David,
Published:	New York :The Guilford Press,c2014.
Description:	xviii, 318 p. :ill. ;25 cm.
Subject:	Bayesian statistical decision theory.
ISBN:	9781462516513 :

Bayesian statistics for the social sciences /
Kaplan, David,1955-

Bayesian statistics for the social sciences /David Kaplan. - New York :The Guilford Press,c2014. - xviii, 318 p. :ill. ;25 cm. - Methodology in the social sciences.

Includes bibliographical references and indexes.

" 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 the 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. "--

ISBN: 9781462516513 :NT$1602

LCCN: 2014017208Subjects--Topical Terms:

182005
Bayesian statistical decision theory.

LC Class. No.: HA29 / .K344 2014

Dewey Class. No.: 519.5/42

Bayesian statistics for the social sciences /
LDR:02074cam 2200217 a 450 001 435617
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008 141201s2014 nyua b 001 0 eng
010 $a 2014017208
020 $a 9781462516513 : $c NT$1602
035 $a 18135908
040 $a DLC $b eng $c DLC
042 $a pcc
050 0 0 $a HA29 $b .K344 2014
082 0 0 $a 519.5/42 $2 23
084 $a PSY032000 $a MED058200 $a EDU027000 $a SOC027000 $a BUS061000 $2 bisacsh
100 1 $a Kaplan, David, $d 1955- $3 219207
245 1 0 $a Bayesian statistics for the social sciences / $c David Kaplan.
260 $a New York : $b The Guilford Press, $c c2014.
300 $a xviii, 318 p. : $b ill. ; $c 25 cm.
490 0 $a Methodology in the social sciences
504 $a Includes bibliographical references and indexes.
520 $a " 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 the 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. "-- $c Provided by publisher.
650 0 $a Bayesian statistical decision theory. $3 182005
650 0 $a Social sciences $x Statistical methods. $3 182009