Hostname: page-component-74656f9884-npj9w Total loading time: 0 Render date: 2026-11-07T10:34:21.760Z Has data issue: false hasContentIssue false

Self-Administered Field Surveys on Sensitive Topics

Published online by Cambridge University Press:  10 June 2020

Matthew Nanes
Affiliation:
Saint Louis University, St. Louis, MO, USA, e-mail: matthew.nanes@slu.edu, Twitter: @MatthewJNanes
Dotan Haim
Affiliation:
Florida State University, Tallahassee, FL, USA, e-mail: dhaim@fsu.edu, Twitter: @HaimDotan
Rights & Permissions [Opens in a new window]

Abstract

Research on sensitive topics uses a variety of methods to combat response bias on in-person surveys. Increasingly, researchers allow respondents to self-administer responses using electronic devices as an alternative to more complicated experimental approaches. Using an experiment embedded in a survey in the rural Philippines, we test the effects of several such methods on response rates and falsification. We asked respondents a sensitive question about reporting insurgents to the police alongside a nonsensitive question about school completion. We randomly assigned respondents to answer these questions either verbally, through a “forced choice” experiment, or through self-enumeration. We find that self-enumeration significantly reduced nonresponse compared to direct questioning, but find little evidence of differential rates of falsification. Forced choice yielded highly unlikely estimates, which we attribute to nonstrategic falsification. These results suggest that self-administered surveys can be effective for measuring sensitive topics on surveys when response rates are a priority.

Information

Type
Research Article
Copyright
© The Experimental Research Section of the American Political Science Association 2020
Figure 0

Figure 1 Self-Enumeration Screenshot.

Figure 1

Figure 2 Response Rates by Experimental Group.

Figure 2

Figure 3 Affirmative Answers by Experimental Group. Direct and self are observed frequencies, while forced depicts an estimated value. Statistically significant differences are noted in text, and we present regression models in the Supplementary Material.

Supplementary material: Link

Nanes and Haim Dataset

Link
Supplementary material: PDF

Nanes and Haim supplementary material

Online Appendix

Download Nanes and Haim supplementary material(PDF)
PDF 287.9 KB