Emergent principles for the design, implementation, and analysis of cluster-based experiments in social science
By: COOK, Thomas D
.
Material type: 
ArticlePublisher: Thousand Oaks : SAGE, May 2005The Annals of The American Academy of Political and Social Science 599, p. 176-198Abstract: In experimentally designed research, many good reasons exist for assigning groups or clusters to treatments rather than individuals. This article identifies them and offers some principles about them. One emphasizes how statistical power and sample size estimation depend on intraclass correlations, particulary after conditioning on the use of cluster-level covariates. Another stress assigning experimental units at the lowest level of aggregation possible, provide this does not subtly change the research question. A third emphasizes the utility of minimizing and measuirng interunit communication, though neither is easy to achieve. A fourth advises against experiments that are totally black box and so leave program implementaion and process more salient. The last principle involves the utility of describing treatment heterogeneity and estimating its consequences, though causal conclusions about the heterogeneity will be less well warranted compared to conclusions about the intended treatment, every experiment's major focus.
In experimentally designed research, many good reasons exist for assigning groups or clusters to treatments rather than individuals. This article identifies them and offers some principles about them. One emphasizes how statistical power and sample size estimation depend on intraclass correlations, particulary after conditioning on the use of cluster-level covariates. Another stress assigning experimental units at the lowest level of aggregation possible, provide this does not subtly change the research question. A third emphasizes the utility of minimizing and measuirng interunit communication, though neither is easy to achieve. A fourth advises against experiments that are totally black box and so leave program implementaion and process more salient. The last principle involves the utility of describing treatment heterogeneity and estimating its consequences, though causal conclusions about the heterogeneity will be less well warranted compared to conclusions about the intended treatment, every experiment's major focus.
Click on an image to view it in the image viewer
There are no comments for this item.