A Two-stage Binary Optional Randomized Response Model

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 π, the prevalence of sensitive characteristic and ω, 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 [4]. iii ACKNOWLEDGMENTS The work for this thesis was undertaken at the suggestion of Professor Sat Narain Gupta, to whom I wish to express my heartfelt appreciation and eternal gratitude for his assistance and helpful criticism. He has guided me so wisely and always knows the answer. I am also indebted to Professor Scott James Richter and Professor Dohyoung Ryang for the assistance and direction they have given me in this undertaking.