© 2015 – Chapman and Hall/CRC
236 pages | 18 B/W Illus.
This book focuses on two general purpose approaches to data analysis that work well in practice: weighting and imputation. The book takes a very practical approach to the methods, with a number of datasets used to illustrate the key aspects. The datasets are taken from randomized trials, observational studies, and sample surveys. Keeping theoretical details to a minimum, the book is suitable for practitioners with only basic knowledge of statistics. The author’s SAS-based software, which can be used for all the examples, is available online.
"With a firm command of incomplete-data methodology and a smooth narrative, Missing Data Analysis in Practice provides an up-to-date overview of the field. Applied researchers will appreciate the book’s guidance on pragmatic issues like selecting the number of imputations, using transformations, and including the outcome when imputing missing covariates. Attention to finite-population estimation makes the book a valuable bridge between design-based and model-based perspectives. And with extensions to areas like longitudinal analysis, survival analysis, and disclosure avoidance, statisticians will find that the book complements the classic text by Little and Rubin."
—Tom Belin, Department of Biostatistics, UCLA
"If you don’t know missing data, you don’t know data. This slim and readable book covers a range of concepts and techniques for handling missing data in applied statistics."
—Andrew Gelman, Professor of Statistics and Political Science, Columbia University
Introduction. Weighting. Imputation. Multiple Imputation. Parametric Model-Based Imputation. Nonparametric Imputations. Sequential Regression. Multiple Imputation Diagnostics. Missing Data in Longitudinal Studies. Maximum Likelihood Approach. Non-Ignorable Models. Multiple Imputation for Complex Surveys. Other Applications of Multiple Imputation.