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

2008/12/18 by Daniel Corstange · 3 citations
Mathematics · #Survey Sampling and Estimation Techniques

paper · pdf · doi:10.1093/pan/mpn013

openalex publication_date 2008/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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