Statistics for Social and Behavioral Sciences
cover

Applied Multiple Imputation

Advantages, Pitfalls, New Developments and Applications in R

Authors: Kleinke, K., Reinecke, J., Salfrán, D., Spiess, M.

  • 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
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eBook $79.99
price for Mexico (gross)
  • The eBook version of this title will be available soon
  • Due: 29 de abril de 2020
  • ISBN 978-3-030-38164-6
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover $99.99
price for Mexico
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: 1 de abril de 2020
  • ISBN 978-3-030-38163-9
  • Free shipping for individuals worldwide
About this Textbook

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. 

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.

Buy this book

eBook $79.99
price for Mexico (gross)
  • The eBook version of this title will be available soon
  • Due: 29 de abril de 2020
  • ISBN 978-3-030-38164-6
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover $99.99
price for Mexico
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week). Pre-ordered printed titles are excluded from promotions.
  • Due: 1 de abril de 2020
  • ISBN 978-3-030-38163-9
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Applied Multiple Imputation
Book Subtitle
Advantages, Pitfalls, New Developments and Applications in R
Authors
Series Title
Statistics for Social and Behavioral Sciences
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-38164-6
DOI
10.1007/978-3-030-38164-6
Hardcover ISBN
978-3-030-38163-9
Series ISSN
2199-7357
Edition Number
1
Number of Pages
XI, 292
Number of Illustrations
17 b/w illustrations, 3 illustrations in colour
Topics