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Record Nr. |
UNINA9910484708003321 |
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Autore |
Kleinke Kristian |
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Titolo |
Applied Multiple Imputation : Advantages, Pitfalls, New Developments and Applications in R / / by Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XI, 292 p. 20 illus., 3 illus. in color.) |
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Collana |
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Statistics for Social and Behavioral Sciences, , 2199-7365 |
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Disciplina |
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Soggetti |
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Social sciences - Statistical methods |
Psychology - Methodology |
Biometry |
Statistics |
Mathematical statistics - Data processing |
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy |
Psychological Methods |
Biostatistics |
Statistical Theory and Methods |
Statistics and Computing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. . |
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