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


Statistical Methods for Geography
Perspectives on Deviance and Social Control
A Mind at Home with Itself
Transcendental Magic
101 Ground Training Exercises for Every Horse & Handler
Moonology
Encounters: Chinese Language and Culture, Student Book 1
English Grammar for Students of French
The Psychedelic Gospels
Career Guide in Criminal Justice
Construction Site Safety
Basic Finance: An Introduction to Financial Institutions, Investments and Management
Basic Robot Building With LEGO Mindstorms NXT 2.0
Cadillac Desert
Business and Professional Communication
Acting Out Culture
God’s Rascal
Guide to the LEED Green Associate V4 Exam
Developmental Mathematics 
Review Data Analysis Using Regression and Multilevel/Hierarchical Models
There are no reviews yet.