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Replication Data for: Sensitive Survey Questions with Auxiliary Information

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Sensitive Survey Questions with Auxiliary Information
 
Identifier https://doi.org/10.7910/DVN/4FEJZ3
 
Creator Chou, Winston
Imai, Kosuke
Rosenfeld, Bryn
 
Publisher Harvard Dataverse
 
Description Scholars increasingly rely on indirect questioning techniques to reduce social desirability bias and item nonresponse for sensitive survey questions. The major drawback of these approaches, however, is their inefficiency relative to direct questioning. We show how to improve the statistical analysis of the list experiment, randomized response technique, and endorsement experiment by exploiting auxiliary information on the sensitive trait. We apply the proposed methodology to survey experiments conducted among voters in a controversial anti-abortion referendum held during the 2011 Mississippi General Election. By incorporating the official county-level election results, we obtain precinct- and individual-level estimates that are more accurate than standard indirect questioning estimates and occasionally even more efficient than direct questioning. Our simulation studies shed light on the conditions under which our approach can improve efficiency and robustness of estimates based on indirect questioning techniques. Open-source software is available for implementing the proposed methodology
 
Subject Social Sciences
endorsement experiment, item count technique, list experiment, randomized response technique, social desirability bias, survey experiment, unmatched count technique
 
Contributor Rosenfeld, Bryn