Political scientists have long been interested in citizens' support
level for socially sensitive actors such as ethnic minorities,
militant groups, and authoritarian regimes. Attempts to use direct
questioning in surveys, however, have largely yielded unreliable
measures of these attitudes as they are contaminated by social
desirability bias and high non-response rates. In this paper, we
develop a statistical methodology to analyze endorsement
experiments, which recently have been proposed as a possible
solution to this measurement problem. The commonly used statistical
methods are problematic because they cannot properly combine
responses across multiple policy questions, the design feature of a
typical endorsement experiment. We overcome this limitation by
using item response theory to estimate support levels on the same
scale as the ideal points of respondents. We also show how to
extend our model to incorporate a hierarchical structure of data in
order to recoup the loss of statistical efficiency due to indirect
questioning. We illustrate the proposed methodology by applying it
to measure political support for Islamist militant groups in
Pakistan. Simulation studies suggest that the proposed Bayesian
model yields estimates with reasonable levels of bias and
statistical power. Finally, we offer several practical suggestions
for improving the design and analysis of endorsement experiments.
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