{"product_id":"causality-in-a-social-world-isbn-9781118332566","title":"Causality in a Social World","description":"\u003cp\u003e\u003cb\u003e\u003ci\u003eCausality in a Social World\u003c\/i\u003e\u003c\/b\u003e 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.\u003c\/p\u003e \u003cp\u003eThe 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 the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory.\u003c\/p\u003e \u003cp\u003eApplications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.\u003c\/p\u003e Preface xv \u003cp\u003e\u003cb\u003ePart I Overview 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Concepts of moderation, mediation, and spill-over 3\u003c\/p\u003e \u003cp\u003e1.2 Weighting methods for causal inference 10\u003c\/p\u003e \u003cp\u003e1.3 Objectives and organization of the book 11\u003c\/p\u003e \u003cp\u003e1.4 How is this book situated among other publications on related topics? 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Review of causal inference concepts and methods 18\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Causal inference theory 18\u003c\/p\u003e \u003cp\u003e2.2 Applications to Lord’s paradox and Simpson’s paradox 27\u003c\/p\u003e \u003cp\u003e2.3 Identification and estimation 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Review of causal inference designs and analytic methods 40\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Experimental designs 40\u003c\/p\u003e \u003cp\u003e3.2 Quasiexperimental designs 44\u003c\/p\u003e \u003cp\u003e3.3 Statistical adjustment methods 46\u003c\/p\u003e \u003cp\u003e3.4 Propensity score 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Adjustment for selection bias through weighting 76\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Weighted estimation of population parameters in survey sampling 77\u003c\/p\u003e \u003cp\u003e4.2 Weighting adjustment for selection bias in causal inference 80\u003c\/p\u003e \u003cp\u003e4.3 MMWS 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Evaluations of multivalued treatments 100\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Defining the causal effects of multivalued treatments 100\u003c\/p\u003e \u003cp\u003e5.2 Existing designs and analytic methods for evaluating multivalued treatments 102\u003c\/p\u003e \u003cp\u003e5.3 MMWS for evaluating multivalued treatments 112\u003c\/p\u003e \u003cp\u003e5.4 Summary 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Moderation 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Moderated treatment effects: concepts and existing analytic methods 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 What is moderation? 129\u003c\/p\u003e \u003cp\u003e6.2 Experimental designs and analytic methods for investigating explicit moderators 136\u003c\/p\u003e \u003cp\u003e6.3 Existing research designs and analytic methods for investigating implicit moderators 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Marginal mean weighting through stratification for investigating moderated treatment effects 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Existing methods for moderation analyses with quasiexperimental data 159\u003c\/p\u003e \u003cp\u003e7.2 MMWS estimation of treatment effects moderated by individual or contextual characteristics 168\u003c\/p\u003e \u003cp\u003e7.3 MMWS estimation of the joint effects of concurrent treatments 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Cumulative effects of time-varying treatments 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Causal effects of treatment sequences 186\u003c\/p\u003e \u003cp\u003e8.2 Existing strategies for evaluating time-varying treatments 190\u003c\/p\u003e \u003cp\u003e8.3 MMWS for evaluating 2-year treatment sequences 195\u003c\/p\u003e \u003cp\u003e8.4 MMWS for evaluating multiyear sequences of multivalued treatments 204\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Mediation 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Concepts of mediated treatment effects and experimental designs for investigating causal mechanisms 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 214\u003c\/p\u003e \u003cp\u003e9.2 Path coefficients 215\u003c\/p\u003e \u003cp\u003e9.3 Potential outcomes and potential mediators 216\u003c\/p\u003e \u003cp\u003e9.4 Causal effects with counterfactual mediators 219\u003c\/p\u003e \u003cp\u003e9.5 Population causal parameters 222\u003c\/p\u003e \u003cp\u003e9.6 Experimental designs for studying causal mediation 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Existing analytic methods for investigating causal mediation mechanisms 238\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Path analysis and SEM 239\u003c\/p\u003e \u003cp\u003e10.2 Modified regression approach 246\u003c\/p\u003e \u003cp\u003e10.3 Marginal structural models 250\u003c\/p\u003e \u003cp\u003e10.4 Conditional structural models 252\u003c\/p\u003e \u003cp\u003e10.5 Alternative weighting methods 254\u003c\/p\u003e \u003cp\u003e10.6 Resampling approach 256\u003c\/p\u003e \u003cp\u003e10.7 IV method 257\u003c\/p\u003e \u003cp\u003e10.8 Principal stratification 259\u003c\/p\u003e \u003cp\u003e10.9 Sensitivity analysis 261\u003c\/p\u003e \u003cp\u003e10.10 Conclusion 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Investigations of a simple mediation mechanism 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Application example: national evaluation of welfare-to-work strategies 274\u003c\/p\u003e \u003cp\u003e11.2 RMPW rationale 277\u003c\/p\u003e \u003cp\u003e11.3 Parametric RMPW procedure 287\u003c\/p\u003e \u003cp\u003e11.4 Nonparametric RMPW procedure 290\u003c\/p\u003e \u003cp\u003e11.5 Simulation results 292\u003c\/p\u003e \u003cp\u003e11.6 Discussion 295\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 RMPW extensions to alternative designs and measurement 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 RMPW extensions to mediators and outcomes of alternative distributions 301\u003c\/p\u003e \u003cp\u003e12.2 RMPW extensions to alternative research designs 306\u003c\/p\u003e \u003cp\u003e12.3 Alternative decomposition of the treatment effect 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 RMPW extensions to studies of complex mediation mechanisms 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 RMPW extensions to moderated mediation 325\u003c\/p\u003e \u003cp\u003e13.2 RMPW extensions to concurrent mediators 328\u003c\/p\u003e \u003cp\u003e13.3 RMPW extensions to consecutive mediators 340\u003c\/p\u003e \u003cp\u003e13.4 Discussion 355\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Spill-over 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Spill-over of treatment effects: concepts and methods 365\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Spill-over: A nuisance, a trifle, or a focus? 365\u003c\/p\u003e \u003cp\u003e14.2 Stable versus unstable potential outcome values: An example from agriculture 367\u003c\/p\u003e \u003cp\u003e14.3 Consequences for causal inference when spill-over is overlooked 369\u003c\/p\u003e \u003cp\u003e14.4 Modified framework of causal inference 371\u003c\/p\u003e \u003cp\u003e14.5 Identification: Challenges and solutions 376\u003c\/p\u003e \u003cp\u003e14.6 Analytic strategies for experimental and quasiexperimental data 384\u003c\/p\u003e \u003cp\u003e14.7 Summary 387\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Mediation through spill-over 391\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Definition of mediated effects through spill-over in a cluster randomized trial 393\u003c\/p\u003e \u003cp\u003e15.2 Identification and estimation of the spill-over effect in a cluster randomized design 395\u003c\/p\u003e \u003cp\u003e15.3 Definition of mediated effects through spill-over in a multisite trial 402\u003c\/p\u003e \u003cp\u003e15.4 Identification and estimation of spill-over effects in a multisite trial 406\u003c\/p\u003e \u003cp\u003e15.5 Consequences of omitting spill-over effects in causal mediation analyses 412\u003c\/p\u003e \u003cp\u003e15.6 Quasiexperimental application 416\u003c\/p\u003e \u003cp\u003e15.7 Summary 419\u003c\/p\u003e \u003cp\u003eIndex 423\u003c\/p\u003e \u003cb\u003eGuanglei Hong\u003c\/b\u003e, University of Chicago, Department of Comparative Human Development, USA. \u003cp\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003e\u003cb\u003eInnovative strategies for the empirical study of causal effects for social scientists\u003cbr\u003e\u003c\/b\u003e\u003ci\u003e\u003cbr\u003eCausality in a Social World\u003c\/i\u003e introduces a series of weighting methods, often nonparametric in nature, for addressing a wide range of causal questions while minimizing the reliance on model-based assumptions. Rather than fitting research questions to existing statistical tools regardless of their constraining assumptions, this book offers numerous application examples in which causal inquiry is driven by scientific and practical interests. \u003c\/p\u003e \u003cp\u003eKey features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eDemonstrates how each causal relationship can be defined in terms of potential outcomes and identified using optimal experimental designs.\u003c\/li\u003e \u003cli\u003eCompares the identification assumptions invoked by various statistical techniques for analyses of non-experimental data. \u003c\/li\u003e \u003c\/ul\u003e \u003cul\u003e \u003cli\u003eSurveys existing approaches along with presenting innovative new methodologies for causal analysis of moderation, mediation, and spill-over effects with examples from social scientific research.\u003c\/li\u003e \u003cli\u003eProvides detailed descriptions of the weighting procedures.\u003c\/li\u003e \u003cli\u003eOffers user-friendly software packages as well as syntax for the commonly used statistical programs SPSS, SAS, Stata and R.\u003c\/li\u003e \u003c\/ul\u003e \u003ci\u003eCausality in a Social World\u003c\/i\u003e provides guidance to social scientists, graduate and post-graduate students in their empirical investigations of causality. \u003cp\u003eResearchers conducting policy analysis and program evaluation in research companies or in research offices at different levels of the government will also benefit from this book.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988893941989,"sku":"NP9781118332566","price":98.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118332566.jpg?v=1761781952","url":"https:\/\/k12savings.com\/es\/products\/causality-in-a-social-world-isbn-9781118332566","provider":"K12savings","version":"1.0","type":"link"}