Overview
- 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)
Keywords
About this book
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
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.
Bibliographic Information
Book Title: Applied Multiple Imputation
Book Subtitle: Advantages, Pitfalls, New Developments and Applications in R
Authors: Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess
Series Title: Statistics for Social and Behavioral Sciences
DOI: https://doi.org/10.1007/978-3-030-38164-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-38163-9Published: 01 March 2020
Softcover ISBN: 978-3-030-38166-0Published: 01 March 2021
eBook ISBN: 978-3-030-38164-6Published: 29 February 2020
Series ISSN: 2199-7357
Series E-ISSN: 2199-7365
Edition Number: 1
Number of Pages: XI, 292
Number of Illustrations: 20 b/w illustrations, 3 illustrations in colour
Topics: 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