A two-stage binary optional randomized response model

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Jeong Sep Sihm (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/
Sat Gupta

Abstract: Social Desirability Bias (SDB) is the tendency in respondents to answer questions untruthfully in the hope of giving good impression to others. SDB occurs when the survey question is highly sensitive or personal, and responses cause sample statistics to systematically over- or underestimate corresponding population parameters. The Randomized Response Technique (RRT) is one of several methods to get around SDB in surveys involving sensitive questions in a face-to-face interview. In this thesis, we first review some of the existing binary response RRT models. Then, by combining two existing models, we propose a new model--Two-Stage Binary Optional RRT model. Much of the focus is on estimating pi, the prevalence of sensitive characteristic and W, the sensitivity level of the underlying question. We discuss the asymptotic properties of our estimators and present some simulation results. It turns out that the proposed Two-Stage Binary Optional RRT model is more effective than the Optional RRT model proposed by Gupta (2001).

Additional Information

Language: English
Date: 2012
Optional RRT, Parameter Estimation, Randomized Response Technique, Sampling, Two-Stage RRT
Sampling (Statistics)
Surveys $x Statistical methods
Parameter estimation

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