Multivariate Regression Analysis for the Item Count Technique

Published on Jun 1, 2011in Journal of the American Statistical Association3.41
· DOI :10.1198/jasa.2011.ap10415
Kosuke Imai36
Estimated H-index: 36
(Princeton University)
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 article, 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...
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#1Tyler J. VanderWeele (Harvard University)H-Index: 55
#2Miguel A. Hernán (Harvard University)H-Index: 73
#1Dimitar D. Gueorguiev (UCSD: University of California, San Diego)H-Index: 3
#2J MaleskyEdmund (UCSD: University of California, San Diego)H-Index: 20
#1Graeme Blair (Princeton University)H-Index: 5
#2Kosuke Imai (Princeton University)H-Index: 36
#1Judith A. Droitcour (Government Accountability Office)H-Index: 4
#2Rachel A. Caspar (RTI International)H-Index: 3
Last.Trena M. Ezzati (National Center for Health Statistics)H-Index: 6
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Cited By111
#1Erwin H. Bulte (WUR: Wageningen University and Research Centre)H-Index: 42
#2Robert Lensink (UG: University of Groningen)H-Index: 36
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