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Data analysis using regression and multilevel/hierarchical models /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data analysis using regression and multilevel/hierarchical models // Andrew Gelman, Jennifer Hill.
remainder title:	Data Analysis Using Regression & Multilevel/Hierarchical Models
Author:	Gelman, Andrew,
other author:	Hill, Jennifer,
Description:	1 online resource (xxii, 625 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 18 Jul 2016).
Subject:	Regression analysis. -
Online resource:	https://doi.org/10.1017/CBO9780511790942
ISBN:	9780511790942 (ebook)

Data analysis using regression and multilevel/hierarchical models /
Gelman, Andrew,

Data analysis using regression and multilevel/hierarchical models /Data Analysis Using Regression & Multilevel/Hierarchical ModelsAndrew Gelman, Jennifer Hill. - 1 online resource (xxii, 625 pages) :digital, PDF file(s). - Analytical Methods for Social Research. - Analytical Methods for Social Research..

Title from publisher's bibliographic system (viewed on 18 Jul 2016).

Why? -- Concepts and methods from basic probability and statistics -- Linear regression: the basics -- Linear regression: before and after fitting the model -- Logistic regression -- Generalized linear models -- Simulation for checking statistical procedures and model fits -- Causal inference using regression on the treatment variable -- Causal inference using more advanced models -- Multilevel structures -- Multilevel linear models: the basics -- Multilevel linear models: varying slopes, non-nested models, and other complexities.

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

ISBN: 9780511790942 (ebook)Subjects--Topical Terms:

569541
Regression analysis.

LC Class. No.: HA31.3 / .G45 2007

Dewey Class. No.: 519.5/36

Data analysis using regression and multilevel/hierarchical models /
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020 $a 9780511790942 (ebook)
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050 0 0 $a HA31.3 $b .G45 2007
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100 1 $a Gelman, Andrew, $e author. $3 1440162
245 1 0 $a Data analysis using regression and multilevel/hierarchical models / $c Andrew Gelman, Jennifer Hill.
246 3 $a Data Analysis Using Regression & Multilevel/Hierarchical Models
264 1 $a Cambridge : $b Cambridge University Press, $c 2007.
300 $a 1 online resource (xxii, 625 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
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338 $a online resource $b cr $2 rdacarrier
490 0 $a Analytical Methods for Social Research
500 $a Title from publisher's bibliographic system (viewed on 18 Jul 2016).
505 0 $a Why? -- Concepts and methods from basic probability and statistics -- Linear regression: the basics -- Linear regression: before and after fitting the model -- Logistic regression -- Generalized linear models -- Simulation for checking statistical procedures and model fits -- Causal inference using regression on the treatment variable -- Causal inference using more advanced models -- Multilevel structures -- Multilevel linear models: the basics -- Multilevel linear models: varying slopes, non-nested models, and other complexities.
505 0 $a Multilevel logistic regression -- Multilevel generalized linear models -- Multilevel modeling Bugs and R: the basics -- Fitting multilevel linear and generalized linear models in Bugs and R -- Likelihood and Bayesian inference and computation -- Debugging and speeding convergence -- Sample size and power calculations -- Understanding and summarizing the fitted models -- Analysis of variance -- Causal inference using multilevel models -- Model checking and comparison -- Missing-data imputation -- Six quick tips to improve your regression modeling -- Statistical graphics for research and presentation -- Software.
520 $a Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.
650 0 $a Regression analysis. $3 569541
650 0 $a Multilevel models (Statistics) $3 676820
700 1 $a Hill, Jennifer, $d 1969- $e author. $3 1440163
776 0 8 $i Print version: $z 9780521867061
830 0 $a Analytical Methods for Social Research. $3 1440164
856 4 0 $u https://doi.org/10.1017/CBO9780511790942