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Sensitive Questions, Truthful Answers? Modeling the List Experiment with LISTIT

Published online by Cambridge University Press:  04 January 2017

Daniel Corstange
Daniel Corstange*
Affiliation:
Department of Government and Politics, University of Maryland, College Park, MD 20742
*
e-mail: dcorstange@gvpt.umd.edu (corresponding author)
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Abstract

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Standard estimation procedures assume that empirical observations are accurate reflections of the true values of the dependent variable, but this assumption is dubious when modeling self-reported data on sensitive topics. List experiments (a.k.a. item count techniques) can nullify incentives for respondents to misrepresent themselves to interviewers, but current data analysis techniques are limited to difference-in-means tests. I present a revised procedure and statistical estimator called LISTIT that enable multivariate modeling of list experiment data. Monte Carlo simulations and a field test in Lebanon explore the behavior of this estimator.

Information

Type
Research Article
Information
Political Analysis , Volume 17 , Issue 1 , Winter 2009 , pp. 45 - 63
Copyright
Copyright © The Author 2008. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Author's Note: My thanks to Robert Axelrod, Janet Box-Steffensmeier, Sarah Croco, Adam Glynn, Sunshine Hillygus, John Jackson, Luke Keele, Gary King, James Kuklinski, Irfan Nooruddin, Mark Tessler, Ashutosh Varshney, and two anonymous reviewers for their comments and suggestions. Replication materials are available on the Political Analysis web site.

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