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This book provides a self-contained introduction of mixed-effects models and small area estimation techniques. In particular, it focuses on both introducing classical theory and reviewing the latest methods. First, basic issues of mixed-effects models, such as parameter estimation, random effects prediction, variable selection, and asymptotic theory, are introduced. Standard mixed-effects models used in small area estimation, known as the Fay-Herriot model and the nested error regression model, are then introduced. Both frequentist and Bayesian approaches are given to compute predictors of small area parameters of interest. For measuring uncertainty of the predictors, several methods to calculate mean squared errors and confidence intervals are discussed. Various advanced approaches using mixed-effects models are introduced, from frequentist to Bayesian approaches. This book is helpful for researchers and graduate students in fields requiring data analysis skills as well as in mathematical statistics.

Multilevel models (Statistics) --- Hierarchical linear models (Statistics) --- Mixed effects models (Statistics) --- Random coefficient models (Statistics) --- Variance component models (Statistics) --- Mathematical models --- Regression analysis --- Models multinivell (Estadística)

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Quantitative methods in social research --- Programming --- Multilevel models (Statistics) --- Multivariate analysis --- Hierarchical linear models (Statistics) --- Mixed effects models (Statistics) --- Random coefficient models (Statistics) --- Variance component models (Statistics) --- Mathematical models --- Regression analysis --- Data processing --- Mplus. --- Mathematisches Modell. --- Multilevel models (Statistics). --- Sozialwissenschaften. --- Data processing.

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Structural equation modeling. --- Analysis of covariance. --- Linear models (Statistics) --- Multilevel models (Statistics) --- Hierarchical linear models (Statistics) --- Mixed effects models (Statistics) --- Random coefficient models (Statistics) --- Variance component models (Statistics) --- Mathematical models --- Regression analysis --- Models, Linear (Statistics) --- Mathematical statistics --- Statistics --- Covariance analysis --- SEM (Structural equation modeling) --- Multivariate analysis --- Factor analysis --- Path analysis (Statistics) --- Structural equation modeling --- Analysis of covariance

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Quantitative methods in social research --- Information systems --- multivariaat --- regressie-analyse --- wiskundige statistiek --- 303.7.034 --- Multivariabele analyse --(sociaal onderzoek) --- Multilevel models (Statistics) --- Regression analysis. --- Social sciences --- Statistical methods. --- Multilevel models (Statistics). --- 303.7.034 Multivariabele analyse --(sociaal onderzoek) --- Regression analysis --- 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 --- Statistical methods

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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