LEADER 05320nam 2200673 450 001 9910812455703321 005 20230807215947.0 010 $a1-119-03063-3 010 $a1-119-03060-9 010 $a1-119-03064-1 035 $a(CKB)3710000000433528 035 $a(EBL)1895957 035 $a(SSID)ssj0001535751 035 $a(PQKBManifestationID)11893020 035 $a(PQKBTitleCode)TC0001535751 035 $a(PQKBWorkID)11502803 035 $a(PQKB)10701536 035 $a(MiAaPQ)EBC4040672 035 $a(MiAaPQ)EBC1895957 035 $a(Au-PeEL)EBL4040672 035 $a(CaPaEBR)ebr11113791 035 $a(CaONFJC)MIL802205 035 $a(OCoLC)911266364 035 $a(EXLCZ)993710000000433528 100 $a20151110h20152015 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aCausality in a social world $emoderation, meditation and spill-over /$fGuanglei Hong 210 1$aChichester, England :$cWiley,$d2015. 210 4$d©2015 215 $a1 online resource (1031 p.) 300 $aDescription based upon print version of record. 311 $a1-118-33256-3 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCover; Table of Contents; Title page; Preface; Part I: OVERVIEW; 1 Introduction; 1.1 Concepts of moderation, mediation, and spill-over; 1.2 Weighting methods for causal inference; 1.3 Objectives and organization of the book; 1.4 How is this book situated among other publications on related topics?; References; 2 Review of causal inference concepts and methods; 2.1 Causal inference theory; 2.2 Applications to Lord's paradox and Simpson's paradox; 2.3 Identification and estimation; Appendix 2.1: Potential bias in a prima facie effect 327 $aAppendix 2.2: Application of the causal inference theory to Lord's paradox References; 3 Review of causal inference designs and analytic methods; 3.1 Experimental designs; 3.2 Quasi-experimental designs; 3.3 Statistical adjustment methods; 3.4 Propensity score; Appendix 3.A: Potential bias due to the omission of treatment-by-covariate interaction; Appendix 3.B: Variable selection for the propensity score model; References; 4 Adjustment for selection bias through weighting; 4.1 Weighted estimation of population parameters in survey sampling 327 $a4.2 Weighting adjustment for selection bias in causal inference 4.3 MMWS; Appendix 4.A: Proof of MMWS-adjusted mean observed outcome being unbiased for the population average potential outcome; Appendix 4.B: Derivation of MMWS for estimating the treatment effect on the treated; Appendix 4.C: Theoretical equivalence of MMWS and IPTW; Appendix 4.D: Simulations comparing MMWS and IPTW under misspecifications of the functional form of a propensity score model; References; 5 Evaluations of multivalued treatments; 5.1 Defining the causal effects of multivalued treatments 327 $a5.2 Existing designs and analytic methods for evaluating multivalued treatments 5.3 MMWS for evaluating multivalued treatments; 5.4 Summary; Appendix 5.A: Multiple IV for evaluating multivalued treatments; References; Part II: MODERATION; 6 Moderated treatment effects: concepts and existing analytic methods; 6.1 What is moderation?; 6.2 Experimental designs and analytic methods for investigating explicit moderators; 6.3 Existing research designs and analytic methods for investigating implicit moderators 327 $aAppendix 6.A: Derivation of bias in the fixed-effects estimator when the treatment effect is heterogeneous in multisite randomized trials Appendix 6.B: Derivation of bias in the mixed-effects estimator when the probability of treatment assignment varies across sites; Appendix 6.C: Derivation and proof of the population weight applied to mixed-effects models for eliminating bias in multisite randomized trials; References; 7 Marginal mean weighting through stratification for investigating moderated treatment effects; 7.1 Existing methods for moderation analyses with quasi-experimental data 327 $a7.2 MMWS estimation of treatment effects moderated by individual or contextual characteristics 330 $aCausality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in 606 $aMathematical statistics 606 $aResearch$xMethodology 606 $aStatistics$xMethodology 615 0$aMathematical statistics. 615 0$aResearch$xMethodology. 615 0$aStatistics$xMethodology. 676 $a519.5 700 $aHong$b Guanglei$01651978 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910812455703321 996 $aCausality in a social world$94002300 997 $aUNINA LEADER 00658nam a2200181 ic4500 001 991004377534407536 005 20250418112957.0 008 180425s1882 it r a000 0 ita d 040 $aBibl. Interfacoltà T. Pellegrino$bita 082 04$a636 110 2 $aStabilimento sperimentale di zootecnia <$cReggio Emilia>$01816872 245 10$aAtti 1877-1880$nVol. 2. /$cStabilimento sperimentale di zootecnia in Reggio-Emilia 260 $aReggio Emilia :$bTip. Calderini,$c1882 300 $aCIII, 133 p., 11 c. di tav. ;$c30 cm 650 4$aAllevamento$zReggio Emilia$ySec. 19. 912 $a991004377534407536 996 $aAtti 1877-1880$94373889 997 $aUNISALENTO