Applied Multiple Imputation: Advantages, Pitfalls, New Developments and Applications in R (1st ed. 2020) (Statistics for Social and Behavioral Sciences)

By: Jost Reinecke and Kristian Kleinke and Daniel Salfrán and Martin Spiess

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Synopsis: 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.

Book Quality:: Publisher Quality
ISBN-13:: 9783030381646
Related ISBNs:: 9783030381639
Publisher:: Springer International Publishing
Date of Addition:: 05/27/20
Copyrighted By:: Springer
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Computers and Internet, Psychology, Mathematics and Statistics, Medicine, Sociology
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.