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The Statistical Analysis of Misreporting on Sensitive Survey Questions

Published online by Cambridge University Press:  11 April 2017

Gregory Eady
Gregory Eady*
Affiliation:
Department of Political Science, University of Toronto, 100 St. George Street, Toronto, Ontario, Canada,  M5S 3G3. Email: [email protected]
*
*Email: [email protected]
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Abstract

What explains why some survey respondents answer truthfully to a sensitive survey question, while others do not? This question is central to our understanding of a wide variety of attitudes, beliefs, and behaviors, but has remained difficult to investigate empirically due to the inherent problem of distinguishing those who are telling the truth from those who are misreporting. This article proposes a solution to this problem. It develops a method to model, within a multivariate regression context, whether survey respondents provide one response to a sensitive item in a list experiment, but answer otherwise when asked to reveal that belief openly in response to a direct question. As an empirical application, the method is applied to an original large-scale list experiment to investigate whether those on the ideological left are more likely to misreport their responses to questions about prejudice than those on the right. The method is implemented for researchers as open-source software.

Type
Articles
Information
Political Analysis , Volume 25 , Issue 2 , April 2017 , pp. 241 - 259
Copyright
Copyright © The Author(s) 2017. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Author’s note: I am grateful to Andy Guess, Barbora Racevičiūtė, Charles Breton, Cliff van der Linden, Greg Kerr, Matt Hoffmann, Matto Mildenberger, Peter Aronow, Peter Loewen, Sarah Zendel, Seva Gunitsky, and Uyen Hoang for their helpful suggestions and comments. I thank Félix-Antoine Fortin for facilitating access to and providing support for Calcul Québec’s large-scale computing network. Open-source software for the method proposed in this article, misreport, is available for download for the statistical software R from the Comprehensive R Archive Network (CRAN) athttps://cran.r-project.org/package=misreport. Replication files for this article can be found in Eady (2016).

Contributing Editor: Jonathan Katz

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