Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and longitudinal studies, randomized clinical trials, complex sample surveys are used to illustrate both simple, and complex analyses.
Version 0.3 of IVEware, the software developed by the University of Michigan, is used to illustrate analyses. IVEware can multiply impute missing values, analyze multiply imputed data sets, incorporate complex sample design features, and be used for other statistical analyses framed as missing data problems. IVEware can be used under Windows, Linux, and Mac, and with software packages like SAS, SPSS, Stata, and R, or as a stand-alone tool. The data sets for the examples and exercises and the example code files are available to download here. The figures, many of which are available in color, are also available.
Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online.
The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.
A bibliography of literature resources related to the IVEware software and statistical analysis can be found here.