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The validity of empirical research often
relies upon the accuracy of self-reported behavior and beliefs.
Yet, eliciting truthful answers in surveys is challenging especially
when studying sensitive issues such as racial prejudice, corruption,
and support for militant groups. List experiments have attracted
much attention recently as a potential solution to this measurement
problem. Many researchers, however, have used a simple
difference-in-means estimator without being able to efficiently
examine multivariate relationships between respondents'
characteristics and their answers to sensitive items. Moreover, no
systematic means exist to investigate role of underlying
assumptions. We fill these gaps by developing a set of new
statistical methods for list experiments. We identify the commonly
invoked assumptions, propose new multivariate regression estimators,
and develop methods to detect and adjust for potential violations of
key assumptions. For empirical illustrations, we analyze list
experiments concerning racial prejudice. Open source software is
made available to implement the proposed methodology.
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See the Supplementary Materials for computational details of the proposed method.
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See the Some examples of list experiments
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See this page for the information about the project on the elicitation of truthful answers to sensitive survey questions.
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The software package that implements the proposed method is available here for download.
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