Overview

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. This book will be helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems, along with examples of correct analysis techniques.

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9780367734039

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Reviews

""This is a very useful book for applied researchers, especially those working with complex survey samples with stratification, clustering, and weighting. It contains detailed examples and programming codes that can be easily followed and carried out by users of all levels. It has a good balance of statistical methods and practical application of multiple imputation. In most chapters, the authors start by explaining the basic concepts in complete data analysis, then extending the topics to amultiple imputation setting. Relevant data examples appear throughout the text. Additional readings are listed at the end of each chapter to helpmore advanced readers gain a better understanding of the methods and theories underlying the topics presented in the text."" - Qixuan Chen, The American Statistician, October 2020

This is a very useful book for applied researchers, especially those working with complex survey samples with stratification, clustering, and weighting. It contains detailed examples and programming codes that can be easily followed and carried out by users of all levels. It has a good balance of statistical methods and practical application of multiple imputation. In most chapters, the authors start by explaining the basic concepts in complete data analysis, then extending the topics to amultiple imputation setting. Relevant data examples appear throughout the text. Additional readings are listed at the end of each chapter to helpmore advanced readers gain a better understanding of the methods and theories underlying the topics presented in the text. - Qixuan Chen, The American Statistician, October 2020

Author Information

Trivellore Raghunathan is the director of the Survey Research Center in the Institute for Social Research and professor of biostatistics in the School of Public Health at the University of Michigan. He has published numerous papers in a range of statistical and public health journals. His research interests include applied regression analysis, linear models, design of experiments, sample survey methods, and Bayesian inference. Patricia A. Berglund is a senior research associate in the Youth and Social Indicators Program and Survey Methodology Program in the Survey Research Center at the University of Michigan’s Institute for Social Research.

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