Causality in a Social World

Moderation, Mediation and Spill-over

Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Causality in a Social World by Guanglei Hong, Wiley
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Author: Guanglei Hong ISBN: 9781119030607
Publisher: Wiley Publication: June 9, 2015
Imprint: Wiley Language: English
Author: Guanglei Hong
ISBN: 9781119030607
Publisher: Wiley
Publication: June 9, 2015
Imprint: Wiley
Language: English

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 the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory.

Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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 the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory.

Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.

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