04383nam 22006735 450 99641826470331620220623144729.03-030-38164-110.1007/978-3-030-38164-6(CKB)4100000010480300(DE-He213)978-3-030-38164-6(MiAaPQ)EBC6126406(PPN)242979475(EXLCZ)99410000001048030020200229d2020 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierApplied multiple imputation[electronic resource] advantages, pitfalls, new developments and applications in R /by Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (XI, 292 p. 20 illus., 3 illus. in color.)Statistics for Social and Behavioral Sciences,2199-73573-030-38163-3 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.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. .Statistics for Social and Behavioral Sciences,2199-7357Statistics Psychology—MethodologyPsychological measurementR (Computer program language)Statistics for Social Sciences, Humanities, Lawhttps://scigraph.springernature.com/ontologies/product-market-codes/S17040Psychological Methods/Evaluationhttps://scigraph.springernature.com/ontologies/product-market-codes/Y20040Statistics for Life Sciences, Medicine, Health Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/S17030Statistical Theory and Methodshttps://scigraph.springernature.com/ontologies/product-market-codes/S11001Statistics and Computing/Statistics Programshttps://scigraph.springernature.com/ontologies/product-market-codes/S12008Statistics .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.001.422Kleinke Kristianauthttp://id.loc.gov/vocabulary/relators/aut971635Reinecke Jostauthttp://id.loc.gov/vocabulary/relators/autSalfrán Danielauthttp://id.loc.gov/vocabulary/relators/autSpiess Martinauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK996418264703316Applied Multiple Imputation2209003UNISA