Data analysis using regression and multilevel hierarchical models 2nd edition

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data analysis using regression and multilevel hierarchical models 2nd edition

Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman

Data Analysis Using Regression and Multilevel/Hierarchical Models 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. Author resource page: http: //www.stat.columbia.edu/ gelman/arm/
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An Introduction to Multilevel Modeling - basic terms and research examples - John Nezlek

manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces and .
Andrew Gelman

Data Analysis Using Regression and Multilevel/Hierarchical 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. It introduces and demonstrates a variety of models and instructs the reader in how to fit these models using freely available software packages.

Andrew Gelman is known for his expertise on Bayesian statistics. Based on that knowledge he wrote a book in multilevel regression using R and WINbugs. This book aims to be a thorough description of multilevel regression techniques, implementation of these techniques in R and bugs, and a guide on interpreting the results of your analyses. Shortly put, the books excels on all three subjects. Admittedly, this review has been written based on first impressions on the book. But, a sunny day in the park reading this book literally left me to believe that I have some understanding on what this book is trying to achieve.

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. It introduces and demonstrates a variety of models and instructs the reader in how to fit these models using freely available software packages. He has published over articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy.

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A good comprehensive survey of the topics. But, different sections assume different levels of background knowledge, from nearly nothing to grad-level statistics theory. I like their views on the One of the best books on multi-level models. It was a great read and I loved the examples.

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