Skip to main content
SpringerLink
Log in

Indirect Techniques as Alternatives to Randomized Response

  • Chapter
  • First Online:
Indirect Questioning in Sample Surveys

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Biemer, P., & Brown, G. (2005). Model-based estimation of drug prevalence using item count data. Journal of Official Statistics, 21, 287–308.

    Google Scholar 

  • Chaudhuri, A. (2000). Network and adaptive sampling with unequal probabilities. Calcutta Statistical Association Bulletin, 50, 237–253.

    MathSciNet  MATH  Google Scholar 

  • Chaudhuri, A. (2012). Unbiased estimation of a sensitive proportion in general sampling by three non-randomized response schemes. Journal of Statistical Theory and Practice, 6, 376–381.

    Article  Google Scholar 

  • Chaudhuri, A., Bose, M., Dihidar, K. (2005). Sample size restrictive adaptive sampling: an application in estimating localized elements. Journal of Statistical Planning and Inference, 134, 254–267.

    Article  MathSciNet  MATH  Google Scholar 

  • Chaudhuri, A., Bose, M., Ghosh, J.K. (2004). An application of adaptive sampling to estimate highly localized population segments. Journal of Statistical Planning and Inference, 121, 175–189.

    Article  MathSciNet  MATH  Google Scholar 

  • Chaudhuri, A., & Christofides, T.C. (2007). Item count technique in estimating the proportion of people with a sensitive feature. Journal of Statistical Planning and Inference, 137, 589–593.

    Article  MathSciNet  MATH  Google Scholar 

  • Chaudhuri, A., & Christofides, T.C. (2008). Indirect questioning: how to rival randomized response techniques. International Journal of Pure and Applied Mathematics, 43, 283–294.

    MathSciNet  MATH  Google Scholar 

  • Chaudhuri, A., & Mukerjee, R. (1988). Randomized response: theory and techniques. New York: Marcel Dekker.

    MATH  Google Scholar 

  • Chaudhuri, A., & Pal, S. (2002). On certain alternative mean square error estimators in complex survey sampling. Journal of Statistical Planning and Inference, 104, 363–375.

    Article  MathSciNet  MATH  Google Scholar 

  • Christofides, T.C. (2009). Randomized response without a randomization device. Advances and Applications in Statistics, 11, 15–28.

    MathSciNet  MATH  Google Scholar 

  • Coutts, E., & Jann, B. (2011). Sensitive questions in online surveys: experimental results for the randomized response technique (RRT) and the unmatched count technique (UCT). Sociological Methods & Research, 40, 169–193.

    Article  MathSciNet  Google Scholar 

  • Coutts, E., Jann, B., Ivar, K., Anatol-Fiete, N. (2011). Plagiarism in student papers: prevalence estimates using special techniques for sensitive questions. Journal of Economics and Statistics, 231, 749–760.

    Google Scholar 

  • Droitcour, J.A., Larson, E.M., Scheuren, F.J. (2001). The three card method: estimating sensitive items with permanent anonymity of response. In Proceedings of the Social Statistics Section of the American Statistical Association. Alexandria, VA: ASA.

    Google Scholar 

  • Eichhorn, B.H., & Hayre, L.S. (1983). Scrambled randomized response methods for obtaining quantitative data. Journal of Statistical Planning and Inference, 7, 306–316.

    Article  Google Scholar 

  • Esponda, F. (2006). Negative surveys. arXiv:ST/0608176v1

    Google Scholar 

  • Esponda, F., & Guerrero, V.M. (2009). Surveys with negative questions for sensitive items. Statistics and Probability Letters, 79, 2456–2461.

    Article  MathSciNet  MATH  Google Scholar 

  • Gupta, S., Gupta, B., Singh, S. (2002). Estimation of sensitivity level of personal interview survey question. Journal of Statistical Planning and Inference, 100, 239–247.

    Article  MathSciNet  MATH  Google Scholar 

  • Hejri, M.S., Zendehdel, K., Asghari, F., Fotouhi, A., Rashidian, A. (2013). Academic disintegrity among medical students: a randomized response technique study. Medical Education, 47, 144–153.

    Article  Google Scholar 

  • Horvitz, D.G., & Thompson, D.J. (1952). A generalization of sampling without replacement from finite universe. Journal of the American Statistical Association, 47, 663–685.

    Article  MathSciNet  MATH  Google Scholar 

  • Huang, K.-C. (2010). Unbiased estimators of mean, variance and sensitivity level for quantitative characteristics in finite population sampling. Metrika, 71, 341–352.

    Article  MathSciNet  MATH  Google Scholar 

  • Hussain, Z., & Shabbir, J. (2011). On item count technique in survey sampling. Journal of Informatics and Mathematical Sciences, 2, 161–169.

    Google Scholar 

  • Imai, K. (2011). Multivariate regression analysis for the item count technique. Journal of the American Statistical Association, 106, 407–415.

    Article  MathSciNet  MATH  Google Scholar 

  • Jann, B., Jerke, J., Krumpal, I. (2012). Asking sensitive questions using the crosswise model: an experimental survey measuring plagiarism. Public Opinion Quarterly, 76, 32–49.

    Article  Google Scholar 

  • John, F.A.V. St., Edwards-Jones, G., Gibbons, J.M., Jones, J.P.G. (2010). Testing novel methods for assessing rule breaking in conservation. Biological Conservation, 143, 1025–1030.

    Article  Google Scholar 

  • Krebs, C.P., Lindquist, C.H., Warner, T.D., Fisher, B.S., Martin, S.L., Childers, J.M. (2011). Comparing sexual assault prevalence estimates obtained with direct and indirect questioning techniques. Violence Against Women, 17, 219–235.

    Article  Google Scholar 

  • Kutnik, B., Belser, P., Danailova-Trainor, G. (2007). Methodologies for global and national estimation of human trafficking victims: current and future approaches. In Working paper 29, International Labour Office, Geneva.

    Google Scholar 

  • Miller, J.D. (1984). A new survey technique for studying deviant behavior. Ph.D. Thesis, The George Washington University.

    Google Scholar 

  • Miller, J.D. (1985). The nominative technique: a new method of estimating heroin prevalence. NIDA Research Monograph, No. 57, 104–124.

    Google Scholar 

  • Miller, J.D., Cisin, I.H., Harrel, A.V. (1986). A new technique for surveying deviant behavior: item count estimates of marijuana, cocaine and heroin. Paper presented at the annual meeting of the American Association for Public Opinion Research, St. Petersburg, Florida.

    Google Scholar 

  • Pal, S. (2007). Estimating the proportion of people bearing a sensitive issue with an option to item count lists and randomized response. Statistics in Transition, 8, 301–310.

    Google Scholar 

  • Raghavarao, D., & Federer, W.F. (1979). Block total response as an alternative to the randomized response method in surveys. Journal of the Royal Statistical Society: Series B, 41, 40–45.

    MathSciNet  MATH  Google Scholar 

  • Tan, M.T., Tian, G.L., Tang, M.L. (2009). Sample surveys with sensitive questions: a non-randomized response approach. American Statistician, 63, 9–16.

    Article  MathSciNet  Google Scholar 

  • Tian, G.-L., Yu, J.-W., Tang, M.-L., Geng, Z. (2007). A new nonrandomized model for analyzing sensitive questions with binary outcomes. Statistics in Medicine, 26, 4238–4252.

    Article  MathSciNet  Google Scholar 

  • Thompson, S.K. (1992). Sampling. New York: Wiley.

    MATH  Google Scholar 

  • Thompson, S.K., & Seber, G.A.F. (1996). Adaptive sampling. New York: Wiley.

    MATH  Google Scholar 

  • Tsuchiya, T., & Hirai, Y. (2010). Elaborate item count questioning: why do people underreport in item count responses? Survey Research Methods, 4, 139–149.

    Google Scholar 

  • Yates, F., & Grundy, P.M. (1953). Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society: Series B, 15, 253–261.

    MATH  Google Scholar 

  • Yu, J.-W., Tian, G.-L., Tang, M.-L. (2008). Two new models for survey sampling with sensitive characteristic: design and analysis. Metrika, 67, 251–263.

    Article  MathSciNet  Google Scholar 

  • Warner, S.L. (1965). Randomized response: a survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63–69.

    Article  Google Scholar 

  • Xie, H., Kulik, L., Tanin, E. (2011). Privacy-aware collection of aggregate spatial data. Journal of Data and Knowledge Engineering, 70, 576–595.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Applied Statistics Unit Indian Statistical Institute, Kolkata, India

    Arijit Chaudhuri

  2. Department of Mathematics and Statistics, University of Cyprus, Nicosia, Cyprus

    Tasos C. Christofides

Authors
  1. Arijit Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tasos C. Christofides
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Chaudhuri, A., Christofides, T.C. (2013). Indirect Techniques as Alternatives to Randomized Response. In: Indirect Questioning in Sample Surveys. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36276-7_6

Download citation

Publish with us

Policies and ethics

Search

Navigation