Applied Multiple Imputation
Advantages, Pitfalls, New Developments and Applications in R
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Beschreibung
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.
Provides an introduction to missing data and multiple imputation for students and applied researchers Features numerous step-by-step tutorials in R with supplementary R code and data sets Discusses the advantages and pitfalls of multiple imputation, and presents current developments in the field
Autor*in
Kristian Kleinke
Themen in »Applied Multiple Imputation«
missing data multiple imputation joint modeling conditional modeling consequences of misspecification R packages norm, pan and mice statistical methods missing values incompletely observed data sets quality diagnostics statistical inference
Stimmen zu »Applied Multiple Imputation«
“This is an interesting book encouraging the application of the content presented.” (Maria de Ridder, ISCB News, iscb.info, Issue 70, December, 2020)
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