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Title: Replication data for: Multivariate Continuous Blocking to Improve Political Science Experiments      
dateReleased:
10-03-2014
downloadURL: http://hdl.handle.net/1902.1/18341
ID:
hdl:1902.1/18341
description:
Political scientists use randomized treatment assignments to aid causal inference in field experiments, psychological laboratories, and survey research. Political research can do considerably better than completely randomized designs, but few political science experiments combine random treatment assignment with blocking on a rich set of background covariates. We describe high-dimensional multivariate blocking, including on continuous covariates, detail its statistical and political advantages over complete randomization, introduce a particular algorithm, and propose a procedure to mitigate unit interference in experiments. We demonstrate the performance of our algorithm in simulations and three field experiments from campaign politics and education.
description:
Ryan Moore, 2012, "Replication data for: Multivariate Continuous Blocking to Improve Political Science Experiments", http://hdl.handle.net/1902.1/18341, Harvard Dataverse, V2
name:
Ryan Moore
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997