Journal
POLITICAL ANALYSIS
Volume 17, Issue 1, Pages 45-63Publisher
OXFORD UNIV PRESS
DOI: 10.1093/pan/mpn013
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Funding
- A National Science Foundation Dissertation Improvement Grant
- University of Michigan's Vaclav Havel Dissertation Fellowship
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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.
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