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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 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.

Mathematical statistics --- Multilevel models (Statistics) --- 519.536 --- Hierarchical linear models (Statistics) --- Mixed effects models (Statistics) --- Random coefficient models (Statistics) --- Variance component models (Statistics) --- Multilevel models (Statistics). --- Regression analysis. --- Methoden en technieken --- statistiek. --- wiskundige statistiek --- regressie-analyse --- #SBIB:303H520 --- 519.2 --- Regression analysis --- Analysis, Regression --- Linear regression --- Regression modeling --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Mathematical models --- Multivariate analysis --- Structural equation modeling

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This book provides a uniquely accessible introduction to multilevel modeling, a powerful tool for analyzing relationships between an individual-level dependent variable, such as student reading achievement, and individual-level and contextual explanatory factors, such as gender and neighborhood quality. Helping readers build on the statistical techniques they already know, Robert Bickel emphasizes the parallels with more familiar regression models, shows how to do multilevel modeling using SPSS, and demonstrates how to interpret the results. He discusses the strengths and limitations of multilevel analysis and explains specific circumstances in which it offers (or does not offer) methodological advantages over more traditional techniques. Over 300 dataset examples from research on educational achievement, income attainment, voting behavior, and other timely issues are presented in numbered procedural steps.

Quantitative methods in social research --- Mathematical statistics --- Statistics --- Social sciences --- Multilevel models (Statistics) --- Regression analysis. --- Sciences sociales --- Modèles multiniveaux (Statistique) --- Analyse de régression --- Research --- Mathematical models. --- Recherche --- Modèles mathématiques --- #SBIB:303H520 --- 519.8 --- 303 --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Operational research --- Methoden bij sociaalwetenschappelijk onderzoek --- 303 Methoden bij sociaalwetenschappelijk onderzoek --- 519.8 Operational research --- Modèles multiniveaux (Statistique) --- Analyse de régression --- Modèles mathématiques --- Regression analysis --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Hierarchical linear models (Statistics) --- Mixed effects models (Statistics) --- Random coefficient models (Statistics) --- Variance component models (Statistics) --- Mathematical models --- Research&delete& --- Multilevel models (Statistics).