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Applied Multiple Imputation

Advantages, Pitfalls, New Developments and Applications in R

  • Textbook
  • © 2020

Overview

Authors:
  1. Kristian Kleinke

    1. Department of Education Studies and Psychology, University of Siegen, Siegen, Germany

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  2. Jost Reinecke

    1. Faculty of Sociology, University of Bielefeld, Bielefeld, Germany

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  3. Daniel Salfrán

    1. Institute of Psychology, University of Hamburg, Hamburg, Germany

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  4. Martin Spiess

    1. Institute of Psychology, University of Hamburg, Hamburg, Germany

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  • 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

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

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xi
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  2. Introduction and Basic Concepts

    • Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 1-22

  3. Missing Data Mechanism and Ignorability

    • Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 23-52

  4. Missing Data Methods

    • Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 53-83

  5. Multiple Imputation: Theory

    • Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 85-131

  6. Multiple Imputation: Application

    • Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 133-217

  7. Multiple Imputation: New Developments

    • Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
    Pages 219-256

  8. Back Matter

    Pages 257-292
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Keywords

About this book

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. 

Reviews

“This is an interesting book encouraging the application of the content presented.” (Maria de Ridder, ISCB News, iscb.info, Issue 70, December, 2020)

Authors and Affiliations

  • Department of Education Studies and Psychology, University of Siegen, Siegen, Germany

    Kristian Kleinke

  • Faculty of Sociology, University of Bielefeld, Bielefeld, Germany

    Jost Reinecke

  • Institute of Psychology, University of Hamburg, Hamburg, Germany

    Daniel Salfrán, Martin Spiess

About the authors

Kristian Kleinke received his PhD from the University of Bielefeld and is currently an interim Professor of Psychological Methods and General Psychology at the University of Siegen, Germany. His primary research interests include missing data and multiple imputation. His methodological research focuses on multiple imputation solutions for complex data structures like panel data and “non-normal” missing data problems, i.e. when convenient distributional assumptions of the standard MI procedures are violated.

Jost Reinecke is a Professor of Quantitative Methods of Empirical Social Research at the University of Bielefeld, Germany. His current methodological research focuses on growth curve and growth mixture models and the development of techniques related to multiple imputation in complex survey designs. His substantive research focuses on the development of adolescents' delinquent behavior and relationships between group-focused enmity and individual and contextual variables.

Daniel Salfrán was a member of the Applied Mathematics Department and the Cryptography Group at the University of Havana, Cuba, where he worked on a spatial stochastic model for Dengue epidemics. He received his PhD from the University of Hamburg, Germany and is currently lecturer at the Institute for Psychology, University of Hamburg. His research focuses on robust methods to generate multiple imputations.

Martin Spiess is a Professor of Psychological Methods and Statistics at the University of Hamburg, Germany. He studied Psychology, received his PhD in Statistics on the estimation of categorical panel models and was a Research Assistant at the German Institute for Economic Research (DIW). His current research focuses on the estimation of regression and panel data models and techniques to compensate for missing units and missing items.

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