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Title: Replication data for: Designing and Analyzing Randomized Experiments: Application to a Japanese Election Survey Experiment      
dateReleased:
06-28-2010
downloadURL: http://hdl.handle.net/1902.1/JMFHKLRCXS
ID:
hdl:1902.1/JMFHKLRCXS
description:
Randomized experiments are becoming increasingly common in political science. Despite their well-known advantages over observational studies, randomized experiments are not free from complications. In particular, researchers often cannot force subjects to comply with treatment assignment and to provide the requested information. Furthermore, simple randomization of treatments remains the most commonly used method in the discipline even though more efficient procedures are available. Building on the recent statistical literature, we address these methodological issues by offering general recommendations for designing and analyzing randomized experiments to improve the validity and efficiency of causal inference. We also develop a new statistical methodology to explore causal heterogeneity. The proposed methods are applied to a survey experiment conducted during Japan's 2004 Upper House election, where randomly selected voters were encouraged to obtain policy information from political parties' websites. An R package is publicly available for implementing various methods useful for designing and analyzing randomized experiments.
description:
Yusaku Horiuchi; Kosuke Imai; Naoko Taniguchi, 2010, "Replication data for: Designing and Analyzing Randomized Experiments: Application to a Japanese Election Survey Experiment", http://hdl.handle.net/1902.1/JMFHKLRCXS, Harvard Dataverse, V4
name:
Yusaku Horiuchi
Kosuke Imai
Naoko Taniguchi
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997