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.
Additional ISBNs: 9780521867061, 0521867061, 9780511266836, 0511266839


Beef Production and Management Decisions
Developmental Mathematics
Jack Canfield's Key to Living the Law of Attraction
13 Things Mentally Strong People Don't Do
Finite Mathematics for Business, Economics, Life Sciences, and Social Sciences
Elementary & Intermediate Algebra for College Students
Introductory Statistical Inference 
Review Data Analysis Using Regression and Multilevel/Hierarchical Models
There are no reviews yet.