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Bayesian analysis of randomized response sum score variables. (English) Zbl 1270.62059

Summary: The randomized-response (RR) technique is an effective survey method when collecting sensitive information. In this technique, a probability mechanism using randomization devices is commonly involved in answering to sensitive questions. In order to evaluate the survey at the most accurate extend, self-protection (SP) is introduced to describe the responses by participants who give the evasive answer without taking the result of the randomization device into account. In this study, we propose a Bayesian approach to modeling RR sum score variables under SP assumption. RR data from a Dutch survey on non-compliance with social security regulation in 2004 is used to demonstrate the proposed models.

MSC:

62F15 Bayesian inference
62D05 Sampling theory, sample surveys
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[1] DOI: 10.1023/A:1020910605990 · doi:10.1023/A:1020910605990
[2] DOI: 10.1016/j.jspi.2007.10.017 · Zbl 1182.62014 · doi:10.1016/j.jspi.2007.10.017
[3] DOI: 10.1080/03610910600716548 · Zbl 1093.62014 · doi:10.1080/03610910600716548
[4] DOI: 10.1007/BF02595813 · Zbl 1039.62010 · doi:10.1007/BF02595813
[5] Chaudhuri A., Randomized Response: Theory and Techniques (1988)
[6] DOI: 10.1214/07-AOAS135 · doi:10.1214/07-AOAS135
[7] DOI: 10.1080/03610928908829913 · Zbl 0696.62014 · doi:10.1080/03610928908829913
[8] DOI: 10.1214/ss/1177011136 · Zbl 1386.65060 · doi:10.1214/ss/1177011136
[9] DOI: 10.1111/j.1467-9868.2006.00554.x · Zbl 1110.62006 · doi:10.1111/j.1467-9868.2006.00554.x
[10] DOI: 10.2307/2347565 · Zbl 0825.62407 · doi:10.2307/2347565
[11] Greenberg B., J. Amer. Statist. Assoc. 64 pp 529– (1969)
[12] DOI: 10.1093/biomet/57.1.97 · Zbl 0219.65008 · doi:10.1093/biomet/57.1.97
[13] DOI: 10.1016/j.jspi.2004.10.005 · Zbl 1088.62014 · doi:10.1016/j.jspi.2004.10.005
[14] DOI: 10.1080/00401706.1992.10485228 · doi:10.1080/00401706.1992.10485228
[15] Mangat N., J. Roy. Statist. Soc. Ser. B 56 pp 93– (1994)
[16] DOI: 10.1016/S0167-9473(96)00075-8 · Zbl 0900.62144 · doi:10.1016/S0167-9473(96)00075-8
[17] Oh M., J. Kor. Statist. Soc. 23 pp 463– (1994)
[18] DOI: 10.1080/01621459.1987.10478469 · doi:10.1080/01621459.1987.10478469
[19] DOI: 10.1037/0033-2909.87.1.209 · doi:10.1037/0033-2909.87.1.209
[20] DOI: 10.1007/978-94-007-0789-4 · doi:10.1007/978-94-007-0789-4
[21] DOI: 10.1016/j.jspi.2009.01.014 · Zbl 1368.62024 · doi:10.1016/j.jspi.2009.01.014
[22] DOI: 10.2307/270874 · doi:10.2307/270874
[23] DOI: 10.1111/1467-9868.00353 · Zbl 1067.62010 · doi:10.1111/1467-9868.00353
[24] Tian G., Statist. Infer. 1 pp 13– (2009)
[25] Unnikrishnan N., Sankhya: Ind. J. Statist. Ser. B 61 pp 422– (1999)
[26] DOI: 10.1111/j.1467-842X.2009.00552.x · Zbl 1336.62228 · doi:10.1111/j.1467-842X.2009.00552.x
[27] DOI: 10.1080/01621459.1965.10480775 · Zbl 1298.62024 · doi:10.1080/01621459.1965.10480775
[28] DOI: 10.1080/01621459.1979.10481639 · doi:10.1080/01621459.1979.10481639
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