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Applied Multiple Imputation

Advantages, Pitfalls, New Developments and Applications in R

  • Kristian Kleinke
  • Jost Reinecke
  • Daniel Salfrán
  • Martin Spiess
Textbook

Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 1-22
  3. Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 23-52
  4. Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 53-83
  5. Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 85-131
  6. Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 133-217
  7. Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 219-256
  8. Back Matter
    Pages 257-292

About this book

Introduction

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. 

Keywords

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

Authors and affiliations

  • Kristian Kleinke
    • 1
  • Jost Reinecke
    • 2
  • Daniel Salfrán
    • 3
  • Martin Spiess
    • 4
  1. 1.Department of Education Studies and PsychologyUniversity of SiegenSiegenGermany
  2. 2.Faculty of SociologyUniversity of BielefeldBielefeldGermany
  3. 3.Institute of PsychologyUniversity of HamburgHamburgGermany
  4. 4.Institute of PsychologyUniversity of HamburgHamburgGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-38164-6
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-38163-9
  • Online ISBN 978-3-030-38164-6
  • Series Print ISSN 2199-7357
  • Series Online ISSN 2199-7365
  • Buy this book on publisher's site