Causality in a social world: moderation, meditation and spill-over
Causality 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,...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
West Sussex
Wiley
2015
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| Subjects: | |
| Online Access: | FRO01 UBG01 Volltext |
| Summary: | Causality 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 |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | 1 online resource |
| ISBN: | 1119030633 1119030641 9781119030638 9781119030645 |
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| 505 | 8 | |a Title Page; Copyright Page; Contents; Preface; Part I Overview; Chapter 1 Introduction; 1.1 Concepts of moderation, mediation, and spill-over; 1.1.1 Moderated treatment effects; 1.1.2 Mediated treatment effects; 1.1.3 Spill-over effects of a treatment; 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; Chapter 2 Review of causal inference concepts and methods; 2.1 Causal inference theory; 2.1.1 Attributes versus causes | |
| 505 | 8 | |a 2.1.2 Potential outcomes and individual-specific causal effects2.1.3 Inference about population average causal effects; 2.1.3.1 Prima facie effect; 2.1.3.2 Ignorability assumption; 2.2 Applications to Lordś paradox and Simpsonś paradox; 2.2.1 Lordś paradox; 2.2.2 Simpsonś paradox; 2.3 Identification and estimation; 2.3.1 Selection bias; 2.3.2 Sampling bias; 2.3.3 Estimation efficiency; Appendix 2.1: Potential bias in a prima facie effect; Appendix 2.2: Application of the causal inference theory to Lord's paradox; References | |
| 505 | 8 | |a Chapter 3 Review of causal inference designs and analytic methods3.1 Experimental designs; 3.1.1 Completely randomized designs; 3.1.2 Randomized block designs; 3.1.3 Covariance adjustment for improving efficiency; 3.1.4 Multilevel experimental designs; 3.2 Quasiexperimental designs; 3.2.1 Nonequivalent comparison group designs; 3.2.2 Other quasiexperimental designs; 3.3 Statistical adjustment methods; 3.3.1 ANCOVA and multiple regression; 3.3.1.1 ANCOVA for removing selection bias; 3.3.1.2 Potential pitfalls of ANCOVA with a vast between-group difference | |
| 505 | 8 | |a 3.3.1.3 Bias due to model misspecification3.3.2 Matching and stratification; 3.3.3 Other statistical adjustment methods; 3.3.3.1 The IV method; 3.3.3.2 DID analysis; 3.4 Propensity score; 3.4.1 What is a propensity score?; 3.4.2 Balancing property of the propensity score; 3.4.3 Pooling conditional treatment effect estimate: Matching, stratification, and covariance adjustment; 3.4.3.1 Propensity score matching; 3.4.3.2 Propensity score stratification; 3.4.3.3 Covariance adjustment for the propensity score; 3.4.3.4 Sensitivity analysis | |
| 505 | 8 | |a Appendix 3.A: Potential bias due to the omission of treatment-by-covariate interactionAppendix 3.B: Variable selection for the propensity score model; References; Chapter 4 Adjustment for selection bias through weighting; 4.1 Weighted estimation of population parameters in survey sampling; 4.1.1 Simple random sample; 4.1.2 Proportionate sample; 4.1.3 Disproportionate sample; 4.2 Weighting adjustment for selection bias in causal inference; 4.2.1 Experimental result; 4.2.2 Quasiexperimental result; 4.2.3 Sample weight for bias removal; 4.2.4 IPTW for bias removal; 4.3 MMWS | |
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| contents | Title Page; Copyright Page; Contents; Preface; Part I Overview; Chapter 1 Introduction; 1.1 Concepts of moderation, mediation, and spill-over; 1.1.1 Moderated treatment effects; 1.1.2 Mediated treatment effects; 1.1.3 Spill-over effects of a treatment; 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; Chapter 2 Review of causal inference concepts and methods; 2.1 Causal inference theory; 2.1.1 Attributes versus causes 2.1.2 Potential outcomes and individual-specific causal effects2.1.3 Inference about population average causal effects; 2.1.3.1 Prima facie effect; 2.1.3.2 Ignorability assumption; 2.2 Applications to Lordś paradox and Simpsonś paradox; 2.2.1 Lordś paradox; 2.2.2 Simpsonś paradox; 2.3 Identification and estimation; 2.3.1 Selection bias; 2.3.2 Sampling bias; 2.3.3 Estimation efficiency; Appendix 2.1: Potential bias in a prima facie effect; Appendix 2.2: Application of the causal inference theory to Lord's paradox; References Chapter 3 Review of causal inference designs and analytic methods3.1 Experimental designs; 3.1.1 Completely randomized designs; 3.1.2 Randomized block designs; 3.1.3 Covariance adjustment for improving efficiency; 3.1.4 Multilevel experimental designs; 3.2 Quasiexperimental designs; 3.2.1 Nonequivalent comparison group designs; 3.2.2 Other quasiexperimental designs; 3.3 Statistical adjustment methods; 3.3.1 ANCOVA and multiple regression; 3.3.1.1 ANCOVA for removing selection bias; 3.3.1.2 Potential pitfalls of ANCOVA with a vast between-group difference 3.3.1.3 Bias due to model misspecification3.3.2 Matching and stratification; 3.3.3 Other statistical adjustment methods; 3.3.3.1 The IV method; 3.3.3.2 DID analysis; 3.4 Propensity score; 3.4.1 What is a propensity score?; 3.4.2 Balancing property of the propensity score; 3.4.3 Pooling conditional treatment effect estimate: Matching, stratification, and covariance adjustment; 3.4.3.1 Propensity score matching; 3.4.3.2 Propensity score stratification; 3.4.3.3 Covariance adjustment for the propensity score; 3.4.3.4 Sensitivity analysis Appendix 3.A: Potential bias due to the omission of treatment-by-covariate interactionAppendix 3.B: Variable selection for the propensity score model; References; Chapter 4 Adjustment for selection bias through weighting; 4.1 Weighted estimation of population parameters in survey sampling; 4.1.1 Simple random sample; 4.1.2 Proportionate sample; 4.1.3 Disproportionate sample; 4.2 Weighting adjustment for selection bias in causal inference; 4.2.1 Experimental result; 4.2.2 Quasiexperimental result; 4.2.3 Sample weight for bias removal; 4.2.4 IPTW for bias removal; 4.3 MMWS |
| ctrlnum | (ZDB-35-WIC)ocn911266364 (OCoLC)913025795 (DE-599)BVBBV043896930 |
| dewey-full | 122 |
| dewey-hundreds | 100 - Philosophy & psychology |
| dewey-ones | 122 - Causation |
| dewey-raw | 122 |
| dewey-search | 122 |
| dewey-sort | 3122 |
| dewey-tens | 120 - Epistemology, causation, humankind |
| discipline | Soziologie Philosophie Wirtschaftswissenschaften |
| format | Electronic eBook |
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| id | DE-604.BV043896930 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:37:54Z |
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| language | English |
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| spelling | Hong, Guanglei aut Causality in a social world moderation, meditation and spill-over Guanglei Hong West Sussex Wiley 2015 1 online resource txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index Title Page; Copyright Page; Contents; Preface; Part I Overview; Chapter 1 Introduction; 1.1 Concepts of moderation, mediation, and spill-over; 1.1.1 Moderated treatment effects; 1.1.2 Mediated treatment effects; 1.1.3 Spill-over effects of a treatment; 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; Chapter 2 Review of causal inference concepts and methods; 2.1 Causal inference theory; 2.1.1 Attributes versus causes 2.1.2 Potential outcomes and individual-specific causal effects2.1.3 Inference about population average causal effects; 2.1.3.1 Prima facie effect; 2.1.3.2 Ignorability assumption; 2.2 Applications to Lordś paradox and Simpsonś paradox; 2.2.1 Lordś paradox; 2.2.2 Simpsonś paradox; 2.3 Identification and estimation; 2.3.1 Selection bias; 2.3.2 Sampling bias; 2.3.3 Estimation efficiency; Appendix 2.1: Potential bias in a prima facie effect; Appendix 2.2: Application of the causal inference theory to Lord's paradox; References Chapter 3 Review of causal inference designs and analytic methods3.1 Experimental designs; 3.1.1 Completely randomized designs; 3.1.2 Randomized block designs; 3.1.3 Covariance adjustment for improving efficiency; 3.1.4 Multilevel experimental designs; 3.2 Quasiexperimental designs; 3.2.1 Nonequivalent comparison group designs; 3.2.2 Other quasiexperimental designs; 3.3 Statistical adjustment methods; 3.3.1 ANCOVA and multiple regression; 3.3.1.1 ANCOVA for removing selection bias; 3.3.1.2 Potential pitfalls of ANCOVA with a vast between-group difference 3.3.1.3 Bias due to model misspecification3.3.2 Matching and stratification; 3.3.3 Other statistical adjustment methods; 3.3.3.1 The IV method; 3.3.3.2 DID analysis; 3.4 Propensity score; 3.4.1 What is a propensity score?; 3.4.2 Balancing property of the propensity score; 3.4.3 Pooling conditional treatment effect estimate: Matching, stratification, and covariance adjustment; 3.4.3.1 Propensity score matching; 3.4.3.2 Propensity score stratification; 3.4.3.3 Covariance adjustment for the propensity score; 3.4.3.4 Sensitivity analysis Appendix 3.A: Potential bias due to the omission of treatment-by-covariate interactionAppendix 3.B: Variable selection for the propensity score model; References; Chapter 4 Adjustment for selection bias through weighting; 4.1 Weighted estimation of population parameters in survey sampling; 4.1.1 Simple random sample; 4.1.2 Proportionate sample; 4.1.3 Disproportionate sample; 4.2 Weighting adjustment for selection bias in causal inference; 4.2.1 Experimental result; 4.2.2 Quasiexperimental result; 4.2.3 Sample weight for bias removal; 4.2.4 IPTW for bias removal; 4.3 MMWS Causality 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 PHILOSOPHY / Epistemology bisacsh Gesellschaft Causation / Social aspects Empirische Sozialforschung (DE-588)4014606-6 gnd rswk-swf Methodologie (DE-588)4139716-2 gnd rswk-swf Kausalanalyse (DE-588)4163511-5 gnd rswk-swf Empirische Sozialforschung (DE-588)4014606-6 s Kausalanalyse (DE-588)4163511-5 s Methodologie (DE-588)4139716-2 s 1\p DE-604 Erscheint auch als Druckausgabe 978-1-118-33256-6 https://onlinelibrary.wiley.com/doi/book/10.1002/9781119030638 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hong, Guanglei Causality in a social world moderation, meditation and spill-over Title Page; Copyright Page; Contents; Preface; Part I Overview; Chapter 1 Introduction; 1.1 Concepts of moderation, mediation, and spill-over; 1.1.1 Moderated treatment effects; 1.1.2 Mediated treatment effects; 1.1.3 Spill-over effects of a treatment; 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; Chapter 2 Review of causal inference concepts and methods; 2.1 Causal inference theory; 2.1.1 Attributes versus causes 2.1.2 Potential outcomes and individual-specific causal effects2.1.3 Inference about population average causal effects; 2.1.3.1 Prima facie effect; 2.1.3.2 Ignorability assumption; 2.2 Applications to Lordś paradox and Simpsonś paradox; 2.2.1 Lordś paradox; 2.2.2 Simpsonś paradox; 2.3 Identification and estimation; 2.3.1 Selection bias; 2.3.2 Sampling bias; 2.3.3 Estimation efficiency; Appendix 2.1: Potential bias in a prima facie effect; Appendix 2.2: Application of the causal inference theory to Lord's paradox; References Chapter 3 Review of causal inference designs and analytic methods3.1 Experimental designs; 3.1.1 Completely randomized designs; 3.1.2 Randomized block designs; 3.1.3 Covariance adjustment for improving efficiency; 3.1.4 Multilevel experimental designs; 3.2 Quasiexperimental designs; 3.2.1 Nonequivalent comparison group designs; 3.2.2 Other quasiexperimental designs; 3.3 Statistical adjustment methods; 3.3.1 ANCOVA and multiple regression; 3.3.1.1 ANCOVA for removing selection bias; 3.3.1.2 Potential pitfalls of ANCOVA with a vast between-group difference 3.3.1.3 Bias due to model misspecification3.3.2 Matching and stratification; 3.3.3 Other statistical adjustment methods; 3.3.3.1 The IV method; 3.3.3.2 DID analysis; 3.4 Propensity score; 3.4.1 What is a propensity score?; 3.4.2 Balancing property of the propensity score; 3.4.3 Pooling conditional treatment effect estimate: Matching, stratification, and covariance adjustment; 3.4.3.1 Propensity score matching; 3.4.3.2 Propensity score stratification; 3.4.3.3 Covariance adjustment for the propensity score; 3.4.3.4 Sensitivity analysis Appendix 3.A: Potential bias due to the omission of treatment-by-covariate interactionAppendix 3.B: Variable selection for the propensity score model; References; Chapter 4 Adjustment for selection bias through weighting; 4.1 Weighted estimation of population parameters in survey sampling; 4.1.1 Simple random sample; 4.1.2 Proportionate sample; 4.1.3 Disproportionate sample; 4.2 Weighting adjustment for selection bias in causal inference; 4.2.1 Experimental result; 4.2.2 Quasiexperimental result; 4.2.3 Sample weight for bias removal; 4.2.4 IPTW for bias removal; 4.3 MMWS PHILOSOPHY / Epistemology bisacsh Gesellschaft Causation / Social aspects Empirische Sozialforschung (DE-588)4014606-6 gnd Methodologie (DE-588)4139716-2 gnd Kausalanalyse (DE-588)4163511-5 gnd |
| subject_GND | (DE-588)4014606-6 (DE-588)4139716-2 (DE-588)4163511-5 |
| title | Causality in a social world moderation, meditation and spill-over |
| title_auth | Causality in a social world moderation, meditation and spill-over |
| title_exact_search | Causality in a social world moderation, meditation and spill-over |
| title_full | Causality in a social world moderation, meditation and spill-over Guanglei Hong |
| title_fullStr | Causality in a social world moderation, meditation and spill-over Guanglei Hong |
| title_full_unstemmed | Causality in a social world moderation, meditation and spill-over Guanglei Hong |
| title_short | Causality in a social world |
| title_sort | causality in a social world moderation meditation and spill over |
| title_sub | moderation, meditation and spill-over |
| topic | PHILOSOPHY / Epistemology bisacsh Gesellschaft Causation / Social aspects Empirische Sozialforschung (DE-588)4014606-6 gnd Methodologie (DE-588)4139716-2 gnd Kausalanalyse (DE-588)4163511-5 gnd |
| topic_facet | PHILOSOPHY / Epistemology Gesellschaft Causation / Social aspects Empirische Sozialforschung Methodologie Kausalanalyse |
| url | https://onlinelibrary.wiley.com/doi/book/10.1002/9781119030638 |
| work_keys_str_mv | AT hongguanglei causalityinasocialworldmoderationmeditationandspillover |