LEADER 04013nam 22006975 450 001 9910484708003321 005 20250408063931.0 010 $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 $aStatistics for Social and Behavioral Sciences,$x2199-7365 311 08$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,$x2199-7365 606 $aSocial sciences$xStatistical methods 606 $aPsychology$xMethodology 606 $aBiometry 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 606 $aPsychological Methods 606 $aBiostatistics 606 $aStatistical Theory and Methods 606 $aStatistics and Computing 615 0$aSocial sciences$xStatistical methods. 615 0$aPsychology$xMethodology. 615 0$aBiometry. 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 14$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 615 24$aPsychological Methods. 615 24$aBiostatistics. 615 24$aStatistical Theory and Methods. 615 24$aStatistics and Computing. 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