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

  • Daniel Corstange (a1)
Abstract

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.

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Corresponding author
e-mail: dcorstange@gvpt.umd.edu (corresponding author)
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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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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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