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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.
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.
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.
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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