Missing Data Analysis in Practice
Chapman and Hall/CRC – 2015 – 224 pages
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.
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.