Vol. 12, No. 7, 2019

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A binary unrelated-question RRT model accounting for untruthful responding

Amber Young, Sat Gupta and Ryan Parks

Vol. 12 (2019), No. 7, 1163–1173

Estimating the prevalence of a sensitive trait in a population is not a simple task due to the general tendency among survey respondents to answer sensitive questions in a way that is socially desirable. Use of randomized response techniques (RRT) is one of several approaches for reducing the impact of this tendency. However, despite the additional privacy provided by RRT models, some respondents may still provide an untruthful response. We consider the impact of untruthful responding on binary unrelated-question RRT models and observe that even if only a small number of respondents lie, a significant bias may be introduced to the model. We propose a binary unrelated-question model that accounts for those respondents who may respond untruthfully. This adds an extra layer of precaution to the estimation of the sensitive trait and decreases the importance of presurvey respondent training. Our results are validated using a simulation study.

model efficiency, optional randomized response models, unrelated-question RRT model, untruthful responding
Mathematical Subject Classification 2010
Primary: 62D05
Received: 2 November 2018
Revised: 23 March 2019
Accepted: 1 May 2019
Published: 12 October 2019

Communicated by Javier Rojo
Amber Young
Department of Statistics
Purdue University
West Lafayette, IN
United States
Sat Gupta
Department of Mathematics and Statistics
University of North Carolina
Greensboro, NC
United States
Ryan Parks
Department of Mathematics and Statistics
University of North Carolina
Greensboro, NC
United States