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The item count technique is a survey methodology that is designed to
elicit respondents' truthful answers to sensitive questions such as
racial prejudice and drug use. The method is also known as the list
experiment or the unmatched count technique and is an alternative to
the commonly used randomized response method. In this paper, I
propose new nonlinear least squares and maximum likelihood
estimators for efficient multivariate regression analysis with the
item count technique. The two-step estimation procedure and the
Expectation Maximization algorithm are developed to facilitate the
computation. Enabling multivariate regression analysis is essential
because researchers are typically interested in knowing how the
probability of answering the sensitive question affirmatively varies
as a function of respondents' characteristics. As an empirical
illustration, the proposed methodology is applied to the 1991
National Race and Politics survey where the investigators used the
item count technique to measure the degree of racial hatred in the
United States. Small-scale simulation studies suggest that the
maximum likelihood estimator can be substantially more efficient
than alternative estimators. Statistical efficiency is an important
concern for the item count technique because indirect questioning
means loss of information.
The software package is made available to implement the
proposed methodology.
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Please see this page for the information about the project on the elicitation of truthful answers to sensitive survey questions.
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Another paper that builds upon this one and further develops statistical methods for the item count technique (or list experiments) is available here for download.
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The software package that implements the proposed method is available here for download.
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This article was selected by the JASA's editor as a featured
article of the issue. |