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This book explores missing data techniques and provides a detailed
and easy-to-read introduction to multiple imputation, covering the
theoretical aspects of the topic and offering hands-on help with
the implementation. It discusses the pros and cons of various
techniques and concepts, including multiple imputation quality
diagnostics, an important topic for practitioners. It also presents
current research and new, practically relevant developments in the
field, and demonstrates the use of recent multiple imputation
techniques designed for situations where distributional assumptions
of the classical multiple imputation solutions are violated. In
addition, the book features numerous practical tutorials for widely
used R software packages to generate multiple imputations (norm,
pan and mice). The provided R code and data sets allow readers to
reproduce all the examples and enhance their understanding of the
procedures. This book is intended for social and health scientists
and other quantitative researchers who analyze incompletely
observed data sets, as well as master's and PhD students with a
sound basic knowledge of statistics.
This book explores missing data techniques and provides a detailed
and easy-to-read introduction to multiple imputation, covering the
theoretical aspects of the topic and offering hands-on help with
the implementation. It discusses the pros and cons of various
techniques and concepts, including multiple imputation quality
diagnostics, an important topic for practitioners. It also presents
current research and new, practically relevant developments in the
field, and demonstrates the use of recent multiple imputation
techniques designed for situations where distributional assumptions
of the classical multiple imputation solutions are violated. In
addition, the book features numerous practical tutorials for widely
used R software packages to generate multiple imputations (norm,
pan and mice). The provided R code and data sets allow readers to
reproduce all the examples and enhance their understanding of the
procedures. This book is intended for social and health scientists
and other quantitative researchers who analyze incompletely
observed data sets, as well as master's and PhD students with a
sound basic knowledge of statistics.
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