Indirect Techniques as Alternatives to Randomized Response

Arijit Chaudhuri and Tasos C. Christofides
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Arijit Chaudhuri: Applied Statistics Unit Indian Statistical Institute
Tasos C. Christofides: University of Cyprus, Department of Mathematics and Statistics

Chapter Chapter 6 in Indirect Questioning in Sample Surveys, 2013, pp 115-149 from Springer

Abstract: Abstract Numerous randomized response techniques have been developed to handle the case of stigmatizing characteristics. Warner’s (1965) pioneering technique was just the beginning. One of the main disadvantages of randomized response techniques is the fact that participants often are very skeptical about the whole process because, either they do not understand it or because they feel that their privacy is not really protected. In addition, in cases where a randomization device is being used, people think of randomized response as a trick, or as a process which is not really a serious scientific method. Because of these and other drawbacks, for example the fact that randomized response very rarely can be incorporated into survey questionnaires, other alternative methods have been devised. In this chapter, five of those techniques and their variations are presented along with the relevant theory. The most popular one, the Item Count Technique is discussed first, and various versions of it are given. Another technique included in this chapter is the Nominative Technique, which, as explained, can be thought of as an application of network sampling. The Three-Card Method, a simple and easily understood technique is also discussed in brief with theoretical details omitted. A special treatment is given to the recently developed class of Non-Randomized Models. Those are techniques which do not use any device. However, this does not mean that no randomizations are taking place. The last section of the chapter is devoted to the so-called Negative Surveys. Those are surveys where questions are phrased in a negative way so that all but one of the possible answers are true for each and everyone one of the participants.

Keywords: Unbiased Estimator; Simple Random Sample; Academic Dishonesty; Population Proportion; Selection Unit (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-3-642-36276-7_6

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