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

Daniel Corstange

Political Analysis, 2009, vol. 17, issue 1, 45-63

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.

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:17:y:2009:i:01:p:45-63_00

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