Applied Multiple Imputation - Advantages, Pitfalls, New Developments and Applications in R (Paperback, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Statistics for Social and Behavioral Sciences
Release date:	March 2021
First published:	2020
Authors:	Kristian Kleinke • Jost Reinecke • Daniel Salfran • Martin Spiess
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	292
Edition:	1st ed. 2020
ISBN-13:	978-3-03-038166-0
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Social sciences > Sociology, social studies > Social research & statistics > General Books > Social sciences > Psychology > Psychological methodology > General Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
LSN:	3-03-038166-8
Barcode:	9783030381660

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