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Applied multiple imputation : advantages, pitfalls, new developments and applications in R / / by Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess



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Autore: Kleinke Kristian Visualizza persona
Titolo: Applied multiple imputation : advantages, pitfalls, new developments and applications in R / / by Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess Visualizza cluster
Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Edizione: 1st ed. 2020.
Descrizione fisica: 1 online resource (XI, 292 p. 20 illus., 3 illus. in color.)
Disciplina: 001.422
Soggetto topico: Statistics 
Psychology—Methodology
Psychological measurement
R (Computer program language)
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
Persona (resp. second.): ReineckeJost
SalfránDaniel
SpiessMartin
Nota di contenuto: 1 Introduction and Basic Concepts -- 2 Missing Data Mechanism and Ignorability -- 3 Missing Data Methods -- 4 Multiple Imputation: Theory -- 5 Multiple Imputation: Application -- 6 Multiple Imputation: New Developments -- A Appendices -- Index.
Sommario/riassunto: 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. .
Titolo autorizzato: Applied Multiple Imputation  Visualizza cluster
ISBN: 3-030-38164-1
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910484708003321
Lo trovi qui: Univ. Federico II
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Serie: Statistics for Social and Behavioral Sciences, . 2199-7357