找《Data analysis using regression and multilevel/hierarchical models 》完整版书籍，共600多页
Data Analysis Using Regression and Multilevel/Hierarchical Models
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 programming
code 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.
Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia
University. He has published more than 150 articles in statistical theory, methods, and
computation and in applications areas including decision analysis, survey sampling, political
science, public health, and policy. His other books are Bayesian Data Analysis (1995,
second edition 2003) and Teaching Statistics: A Bag of Tricks (2002).
Jennifer Hill is Assistant Professor of Public Affairs in the Department of International
and Public Affairs at Columbia University. She has coauthored articles that have appeared
in the Journal of the American Statistical Association, American Political Science Review,
American Journal of Public Health, Developmental Psychology, the Economic Journal, and
the Journal of Policy Analysis and Management, among others.