LEADER 04358nam 22006735 450 001 9910484708003321 005 20220623144729.0 010 $a3-030-38164-1 024 7 $a10.1007/978-3-030-38164-6 035 $a(CKB)4100000010480300 035 $a(DE-He213)978-3-030-38164-6 035 $a(MiAaPQ)EBC6126406 035 $a(PPN)242979475 035 $a(EXLCZ)994100000010480300 100 $a20200229d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied multiple imputation $eadvantages, pitfalls, new developments and applications in R /$fby Kristian Kleinke, Jost Reinecke, Daniel Salfrán, Martin Spiess 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XI, 292 p. 20 illus., 3 illus. in color.) 225 1 $aStatistics for Social and Behavioral Sciences,$x2199-7357 311 $a3-030-38163-3 327 $a1 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. 330 $aThis book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation. It discusses the pros and cons of various techniques and concepts, including multiple imputation quality diagnostics, an important topic for practitioners. It also presents current research and new, practically relevant developments in the field, and demonstrates the use of recent multiple imputation techniques designed for situations where distributional assumptions of the classical multiple imputation solutions are violated. In addition, the book features numerous practical tutorials for widely used R software packages to generate multiple imputations (norm, pan and mice). The provided R code and data sets allow readers to reproduce all the examples and enhance their understanding of the procedures. This book is intended for social and health scientists and other quantitative researchers who analyze incompletely observed data sets, as well as master?s and PhD students with a sound basic knowledge of statistics. . 410 0$aStatistics for Social and Behavioral Sciences,$x2199-7357 606 $aStatistics  606 $aPsychology?Methodology 606 $aPsychological measurement 606 $aR (Computer program language) 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 606 $aPsychological Methods/Evaluation$3https://scigraph.springernature.com/ontologies/product-market-codes/Y20040 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 615 0$aStatistics . 615 0$aPsychology?Methodology. 615 0$aPsychological measurement. 615 0$aR (Computer program language). 615 14$aStatistics for Social Sciences, Humanities, Law. 615 24$aPsychological Methods/Evaluation. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aStatistical Theory and Methods. 615 24$aStatistics and Computing/Statistics Programs. 676 $a001.422 700 $aKleinke$b Kristian$4aut$4http://id.loc.gov/vocabulary/relators/aut$0971635 702 $aReinecke$b Jost$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSalfrán$b Daniel$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSpiess$b Martin$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484708003321 996 $aApplied Multiple Imputation$92209003 997 $aUNINA