A Field Test of Optional Unrelated Question Randomized Response Models: Estimates of Risky Sexual Behaviors

  • Tracy Spears Gill
  • Anna Tuck
  • Sat Gupta
  • Mary Crowe
  • Jennifer Figueroa
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 64)


Recently Gupta et al. (Involve J Math, 2013) introduced optional unrelated question randomized response models for both the binary response and quantitative response to sensitive survey questions. Asymptotic normality was established for the mean estimator of the sensitive variable and for the prevalence estimator of the sensitive characteristic. Asymptotic normality was also established for the sensitivity level estimator in each case using first order approximation. These mathematical results were validated using computer simulations. In this paper, the binary and quantitative response models are utilized in surveys of sensitive behaviors to verify that these results hold true in fieldwork applications. The two sensitive questions of interest in the survey are “Have you ever been told by a healthcare professional that you have a sexually transmitted disease?” and “How many sexual partners have you had in the last 12 months?” The target population was undergraduate students enrolled at UNC Greensboro during the 2012–2013 academic year. Subjects were asked these questions by optional unrelated question RRT, check-box survey method, and by direct face-to-face interview. The results of these three methods are compared to each other, as well as to existing published information on these two sensitive behaviors. Estimates provided by the optional unrelated question randomized response models are in line with the mathematical results in Gupta et al. (Involve J Math, 2013). This study also provides the first estimate of sensitivity level through a fieldwork survey.


Sensitivity Level Sensitive Question Sensitive Behavior Randomize Response Technique Randomization Device 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by NSF grants DMS 0850465 and DBI 0926288.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Tracy Spears Gill
    • 1
  • Anna Tuck
    • 2
  • Sat Gupta
    • 2
  • Mary Crowe
    • 3
  • Jennifer Figueroa
    • 4
  1. 1.School of NursingUniversity of North Carolina at GreensboroGreensboroUSA
  2. 2.Department of Mathematics and StatisticsUniversity of North Carolina at GreensboroGreensboroUSA
  3. 3.Department of Experiential EducationFlorida Southern CollegeDr. LakelandUSA
  4. 4.Department of BiologyUniversity of North Carolina at GreensboroGreensboroUSA

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