Imai, Kosuke. (2011). ``Multivariate Regression Analysis for the Item Count Technique.'' Journal of the American Statistical Association, Vol. 106, No. 494 (June), pp. 407-416. (featured article)

 

  Abstract

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.

  Other Information

Please see this page for the information about the project on the elicitation of truthful answers to sensitive survey questions.
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.
The software package that implements the proposed method is available here for download.
This article was selected by the JASA's editor as a featured article of the issue.

© Kosuke Imai
 Last modified: Wed Sep 7 10:46:57 EDT 2011