Applied multiple imputation : advantages, pitfalls, new developments and applications in R

• 作者： Kleinke, Kristian, author.
• 其他作者：
  • Reinecke, Jost, author.
  • Salfran, Daniel, author.
  • Spiess, Martin, author.
  • SpringerLink (Online service)
• 其他題名：
  • Statistics for social and behavioral sciences.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Statistics for social and behavioral sciences,
• 主題： Multiple imputation (Statistics) , R (Computer program language) , Statistics for Social Sciences, Humanities, Law. , Psychological Methods/Evaluation. , Statistics for Life Sciences, Medicine, Health Sciences. , Statistical Theory and Methods. , Statistics and Computing/Statistics Programs.
• ISBN： 9783030381646 (electronic bk.) 、 9783030381639 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: 1 Introduction and Basic Concepts -- 2 Missing Data Mechanism and Ignorability -- 3 Missing Data Methods -- 4 Multiple Imputation: Theory -- 5 Multiple Imputation: Application -- 6 Multiple Imputation: New Developments -- A Appendices -- Index.
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• 摘要註: 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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