LEADER 04151nam 22005295 450 001 9910980483503321 005 20250218115255.0 010 $a9789819763986 010 $a9819763983 024 7 $a10.1007/978-981-97-6398-6 035 $a(CKB)37627868900041 035 $a(MiAaPQ)EBC31910762 035 $a(Au-PeEL)EBL31910762 035 $a(DE-He213)978-981-97-6398-6 035 $a(OCoLC)1503846674 035 $a(EXLCZ)9937627868900041 100 $a20250218d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Causal Mediation Analysis with R /$fby Anning Hu 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (119 pages) 311 08$a9789819763979 311 08$a9819763975 327 $aIntroduction and Background -- Mediation in the Causal Inference Framework -- Estimation of the Causal Mediation Analysis -- Moving from the Single-Mediator to the Multi-Mediator Question -- Sensitivity Analysis -- Conclusion. 330 $aThis book comprehensively covers various causal mediation analysis (CMA) methods developed across multiple fields, organizing them into a reader-friendly progression of methodological advancements. Interest in the mechanisms that form causal relationships is widespread across various fields, including sociology, demography, economics, political science, psychology, epidemiology, public health, and educational studies, to name a few. Compared to the well-established research focusing on bivariate causality, CMA?the study of mediation mechanisms within the framework of causal inference?requires more complex identification assumptions, estimation methods, and nuanced interpretations of the results. Therefore, to conduct CMA with rigor, one must acquaint themselves with a distinct and systematic body of knowledge that is clearly separate from traditional linear regression modeling or structural equation modelling (SEM). Against this backdrop, the objectives of the proposed book are twofold. Firstly, it aims to offer readers an approachable and engaging explanation of the statistical theories underpinning the diverse methods of CMA. Specifically, we highlight the crucial mediation identification assumptions?a critical aspect frequently neglected by practitioners and educators. Secondly, the book intends to guide readers through detailed, step-by-step examples of applying CMA methods in practical research contexts. Through this approach, readers are anticipated to gain practical skills necessary for addressing their own research or teaching challenges. This book begins with traditional methods that rely on differences or products of coefficients in linear regression modeling, moves on to CMA involving a single mediator, and advances to more sophisticated approaches that manage parallel or sequentially ordered mediators. Additionally, sensitivity analysis is introduced as an important supplementary analytical step. Thus, the content spans from conventional CMA tools to the forefront methodologies that have emerged in recent decades. The book is designed to be self-sufficient, characterized by a balanced and well-integrated presentation of both theory and application. 606 $aSocial sciences$xStatistical methods 606 $aKnowledge, Sociology of 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 606 $aSociology of Knowledge and Discourse 615 0$aSocial sciences$xStatistical methods. 615 0$aKnowledge, Sociology of. 615 14$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 615 24$aSociology of Knowledge and Discourse. 676 $a300.727 700 $aHu$b Anning$01788480 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910980483503321 996 $aStatistical Causal Mediation Analysis with R$94323412 997 $aUNINA