Multiple Imputation in Practice

Name: Multiple Imputation in Practice
Brand: Taylor & Francis Inc
Price: 152.75 AUD
Availability: BackOrder

With Examples Using Iveware

By: Trivellore Raghunathan, Patricia A. Berglund, Peter W. Solenberger

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12 July 2018 | Edition Number 1

At a Glance

No. of Pages
250

Dimensions(cm)
23.4 x 15.6 x 2.1

RRP $171.00

$152.75

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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.

Industry 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

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