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 mult...

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Bibliographic Details

Main Authors:	Kleinke, Kristian (Author), Reinecke, Jost 1957- (Author), Salfrán, Daniel (Author), Spieß, Martin 1960- (Author)
Format:	Book
Language:	English
Published:	Cham, Switzerland Springer 2020
Series:	Statistics for social and behavioral sciences
Subjects:	Statistics  Psychology—Methodology Psychological measurement Imputationstechnik Fehlende Daten R > Programm Hardcover, Softcover / Soziologie/Methoden der empirischen und qualitativen Sozialforschung
Summary:	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
Physical Description:	xi, 292 Seiten
ISBN:	9783030381639