Data Analysis Using Regression & Multilevel Hierarchical Models first published in 2007 is a comprehensive manual for the applied researcher who wants to perform data analysis using linear & nonlinear regression & 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 & instrumental variables as well as multilevel logistic regression & missing-data imputation Practical tips regarding building fitting & understanding are provided throughout