Data Analysis Using Regression and Multilevel/Hierarchical Models

  • Hilbe J
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Abstract

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 and demonstrates a wide variety of models, at the same time instructing the reader in how to fit these models using freely available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen in the authors' own applied research, with pro-gramming code provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental vari-ables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. University. He has published more than 150 articles in statistical theory, methods, and computation and in applications areas including decision analysis, survey sampling, polit-ical science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002).

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APA

Hilbe, J. M. (2009). Data Analysis Using Regression and Multilevel/Hierarchical Models. Journal of Statistical Software, 30(Book Review 3). https://doi.org/10.18637/jss.v030.b03

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