The proposed methods can be implemented via an R package, “list: Statistical Methods for the Item Count Technique and List Experiment” (Blair and Imai ), which is freely available at the Comprehensive R Archive Network (http://cran.r-project.org/package=list). Replication data are available at http://hdl.handle.net/1902.1/21243. Financial support for the survey from Yale's Institute for Social and Policy Studies Field Experiment Initiative and the Macmillan Center for International and Area Studies is gratefully acknowledged. Additional support from the Air Force Office of Scientific Research (Lyall; Grant FA9550-09-1-0314) and the National Science Foundation (Imai; Grant SES–0849715) is also acknowledged. This research was approved by Yale's Human Subjects Committee under IRB protocol #1006006952. We thank Yuki Shiraito for his methodological advice and Aila Matanock, Justin Phillips, and the seminar participants at Kyusyu University, Princeton University, the University of Sydney and the University of Michigan for helpful comments. The editor and three anonymous reviewers provided useful suggestions.
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ARTICLE
Comparing and Combining List and Endorsement Experiments: Evidence from Afghanistan
Article first published online: 12 FEB 2014
DOI: 10.1111/ajps.12086
©2014, Midwest Political Science Association
Additional Information
How to Cite
Blair, G., Imai, K. and Lyall, J. (2014), Comparing and Combining List and Endorsement Experiments: Evidence from Afghanistan. American Journal of Political Science, 58: 1043–1063. doi: 10.1111/ajps.12086
Publication History
- Issue published online: 13 OCT 2014
- Article first published online: 12 FEB 2014
Funded by
- Yale's Institute for Social and Policy Studies Field Experiment Initiative
- Macmillan Center for International and Area Studies
- Air Force Office of Scientific Research. Grant Number: FA9550-09-1-0314
- National Science Foundation. Grant Number: SES–0849715
- Abstract
- Article
- References
- Cited By
List and endorsement experiments are becoming increasingly popular among social scientists as indirect survey techniques for sensitive questions. When studying issues such as racial prejudice and support for militant groups, these survey methodologies may improve the validity of measurements by reducing nonresponse and social desirability biases. We develop a statistical test and multivariate regression models for comparing and combining the results from list and endorsement experiments. We demonstrate that when carefully designed and analyzed, the two survey experiments can produce substantively similar empirical findings. Such agreement is shown to be possible even when these experiments are applied to one of the most challenging research environments: contemporary Afghanistan. We find that both experiments uncover similar patterns of support for the International Security Assistance Force (ISAF) among Pashtun respondents. Our findings suggest that multiple measurement strategies can enhance the credibility of empirical conclusions. Open-source software is available for implementing the proposed methods.
