Many issues of interest to social scientists and policy makers are of a sensitive nature in the sense that they are intrusive, stigmatizing, or incriminating to the respondent. This results in refusals to cooperate or evasive cooperation in studies using self-reports. In a seminal article, Warner (1965) proposed to curb this problem by generating an artificial variability in responses to inoculate the individual meaning of answers to sensitive questions. This procedure was further developed and extended and came to be known as the randomized response (RR) technique. Here, the authors propose a unified treatment for eliciting sensitive binary as well as quantitative information with RR based on a model where the inoculating elements are provided for by the randomization device. The procedure is simple and the authors will argue that its implementation in a computer-assisted setting may have superior practical capabilities.
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2.
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3.
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4.
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5.
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6.
Cruyff , Maarten J.L.F.,Ardo van den Hout,, Peter G.M. van der Heijden, and Ulf Böckenholt.2007. ‘‘Log-Linear Randomized-Response Models Taking Self-Protective Response Behavior into Account.’’Sociological Methods & Research36:266-82.
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10.
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11.
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12.
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13.
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14.
Lang, Stefan.2004. ‘‘Randomized Response: Befragungstechniken zur Vermeidung von Verzerrungen bei sensitiven Fragen’’ (Randomized Response: Questioning Techniques for Curbing Bias when asking Sensitive Questions) . Habilitations-Probevorlesung Universität München, Unstitut für Statistik.
15.
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17.
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19.
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20.
Lensvelt-Mulders, Gerty J. L. M., Peter G. M. van der Heijden, Olav Laudy, and Ger van Gils.2006. ‘‘A Validation of a Computer-Assisted Randomized-Response Survey to Estimate the Prevalence of Fraud in Social Security.’’Journal of the Royal Statistical Society A169:305-18.
21.
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22.
Moriarty, Mark and Frederick Wiseman.1976. ‘‘On the Choice of a Randomization Technique with the Randomized Response Model.’’ Pp. 624-26 in Proceedings of the American Statistical Association, Social Statistics Section. Washington, DC: American Statistical Association .
23.
Musch, Jochen, Arndt Bröder, and Karl C. Klauer.2001. ‘‘Improving Survey Research on the World-Wide Web using the Randomized Response Technique.’’ Pp. 179-92 in Dimensions of Internet Science, edited by U. D. Reips and M. Bosnjak.Lengerich, Germany: Pabst Science Publishers.
24.
Ostapczuk, Martin, Morten Moshagen, Zengmei Zhao, and Jochen Musch.2009. ‘‘Assessing Sensitive Attributes Using the Randomized Response Technique: Evidence for the Importance of Response Symmetry.’’Journal of Educational and Behavioral Statistics34:267-87.
25.
Peeters, Carel F. W.2005. Measuring Politically Sensitive Behavior. Using Probability Theory in the Form of Randomized Response to Estimate Prevalence and Incidence of Misbehavior in the Public Sphere: A Test on Integrity Violations . Amsterdam, the Netherlands: Dynamics of Governance, Vrije Universiteit.
26.
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27.
Tracy, Derrick S. and Naurang S. Mangat.1996. ‘‘Some Developments in Randomized Response Sampling During the Last Decade-A Followup of Review by Chaudhuri and Mukerjee.’’Journal of Applied Statistical Science4:533-44.
28.
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29.
van der Heijden, Peter G. M. , Ger van Gils, Jan Bouts, and Joop J. Hox.2000. ‘‘A Comparison of Randomized Response, Computer-Assisted Self-Interview, and Face-to-Face Direct Questioning: Eliciting Sensitive Information in the Context of Welfare and Unemployment Benefit.’’Sociological Methods & Research28:505-37.
30.
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