期刊
POLITICAL ANALYSIS
卷 17, 期 1, 页码 45-63出版社
OXFORD UNIV PRESS
DOI: 10.1093/pan/mpn013
关键词
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资金
- A National Science Foundation Dissertation Improvement Grant
- University of Michigan's Vaclav Havel Dissertation Fellowship
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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