Multiple Imputation in Practice: With Examples Using IVEware

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CRC Press, Jul 20, 2018 - Mathematics - 264 pages
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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.

 

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Contents

Chapter 1 Basic Concepts
1
Chapter 2 Descriptive Statistics
33
Chapter 3 Linear Models
45
Chapter 4 Generalized Linear Model
63
Chapter 5 Categorical Data Analysis
79
Chapter 6 Survival Analysis
99
Chapter 7 Structural Equation Models
111
Chapter 8 Longitudinal Data Analysis
121
Chapter 9 Complex Survey Data Analysis using BBDESIGN
149
Chapter 10 Sensitivity Analysis
163
Chapter 11 Odds and Ends
181
Appendix A Overview of Data Sets
207
Appendix B IVEware
227
Bibliography
233
Index
247
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About the author (2018)

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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