Non-randomized scrambling models for sensitive quantitative attribute using innocuous characteristics

References

• Lahaut VM, Jansen HA, Van de Mheen D, et al. Non-response bias in a sample survey on alcohol consumption. Alcohol Alcohol. 2002;37(3):256–260. doi: 10.1093/alcalc/37.3.256
   PubMed Web of Science ®Google Scholar
• Birkeland SA, Manson TM, Kisamore JL, et al. A meta-analytic investigation of job applicant faking on personality measures. Int J Sel Assess. 2006;14(4):317–335. doi: 10.1111/ijsa.2006.14.issue-4
   Web of Science ®Google Scholar
• McFarland LA, Ryan AM. Variance in faking across noncognitive measures. J Appl Psychol. 2000;85(5):812.821 doi: 10.1037/0021-9010.85.5.812
   PubMed Web of Science ®Google Scholar
• Fisher RJ. Social desirability bias and the validity of indirect questioning. J Consum Res. 1993;20(2):303–15. doi: 10.1086/jcr.1993.20.issue-2
   Web of Science ®Google Scholar
• King MF, Bruner GC. Social desirability bias: a neglected aspect of validity testing. Psychol Mark. 2000;17(2):79–103. doi: 10.1002/(ISSN)1520-6793
   Web of Science ®Google Scholar
• Singer E, Von Thurn DR, Miller ER. Confidentiality assurances and response: a quantitative review of the experimental literature. Public Opin Q. 1995;59(1):66–77. doi: 10.1086/269458
   Web of Science ®Google Scholar
• Solomon J, Jacobson SK, Wald KD, et al. Estimating illegal resource use at a Ugandan park with the randomized response technique. Hum Dimens Wildl. 2007;12(2):75–88. doi: 10.1080/10871200701195365
   Google Scholar
• Abul-Ela AL, Greenberg GG, Horvitz DG. A multi-proportions randomized response model. J Am Stat Assoc. 1967;62(319):990–1008. doi: 10.1080/01621459.1967.10500910
   Web of Science ®Google Scholar
• Bourke PD. On the analysis of some multivariate randomized response designs for categorical data. J Stat Plan Inference. 1981;5(2):165–170. doi: 10.1016/0378-3758(81)90026-4
   Web of Science ®Google Scholar
• Kim JM, Warde WD. A mixed randomized response model. J Stat Plan Inference. 2005;133(1):211–221. doi: 10.1016/j.jspi.2004.03.011
   Web of Science ®Google Scholar
• Land M, Singh S, Sedory SA. Estimation of a rare sensitive attribute using poisson distribution. Statistics. 2012;46(3):351–360. doi: 10.1080/02331888.2010.524300
   Web of Science ®Google Scholar
• Warner SL. Randomized response: a survey technique for eliminating evasive answer bias. J Am Stat Assoc. 1965;60(309):63–69. doi: 10.1080/01621459.1965.10480775
   PubMed Web of Science ®Google Scholar
• Chaudhuri A, Mukherjee R. Randomized response: theory and techniques. Statistics: Textbooks and Monographs, Marcel Dekker, Inc; 1988.
   Google Scholar
• Chaudhuri A, Christofides TC. Indirect questioning in sample surveys. Berlin, Heidelberg: Springer Science & Business Media; 2013.
   Google Scholar
• Chaudhuri A. Randomized response and indirect questioning techniques in surveys. New York, NY: CRC Press; 2011.
   Google Scholar
• Chaudhuri A, Christofides TC, Rao CR. Data gathering, analysis and protection of privacy through randomized response techniques: qualitative and quantitative human traits. Amsterdam: Elsevier; 2016.
   Google Scholar
• Fox JA. Randomized response and related methods: surveying sensitive data. Los Angeles: Sage Publications; 2015. p. 29.
   Google Scholar
• Tian GL, Tang ML. Incomplete categorical data design: non-randomized response techniques for sensitive questions in surveys. New York, NY: CRC Press; 2013.
   Google Scholar
• Eichhorn BH, Hayre LS. Scrambled randomized response methods for obtaining sensitive quantitative data. J Stat Plan Inference. 1983;7(4):307–316. doi: 10.1016/0378-3758(83)90002-2
   Web of Science ®Google Scholar
• Greenberg BG, Kuebler Jr RR, Abernathy JR, et al. Application of the randomized response technique in obtaining quantitative data. J Am Stat Assoc. 1971;66(334):243–250. doi: 10.1080/01621459.1971.10482248
   Web of Science ®Google Scholar
• Pollock KH, Bek Y. A comparison of three randomized response models for quantitative data. J Am Stat Assoc. 1976;71(356):884–886. doi: 10.1080/01621459.1976.10480963
   Web of Science ®Google Scholar
• Diana G, Perri PF. New scrambled response models for estimating the mean of a sensitive quantitative character. J Appl Stat. 2010;37(11):1875–1890. doi: 10.1080/02664760903186031
   Web of Science ®Google Scholar
• Diana G, Perri PF. A class of estimators for quantitative sensitive data. Stat Papers. 2011;52:3633–650. doi: 10.1007/s00362-009-0273-1
   Web of Science ®Google Scholar
• Murtaza M, Singh S, Hussain Z. Use of correlated scrambling variables in quantitative randomized response technique. Biom J. 2021;63(1):134–147. doi: 10.1002/bimj.v63.1
   PubMed Web of Science ®Google Scholar
• Narjis G, Shabbir J. An efficient new scrambled response model for estimating sensitive population mean in successive sampling. Commun Stat Simul Comput. 2021;27:1–8.
   Google Scholar
• Singh HP, Patidar P. An improved scrambled model for estimating the mean of a sensitive character. Model Assist Stat Appl. 2021;16(2):87–95.
   Google Scholar
• Zahid A, Masood S, Mubarik S, et al. An efficient application of scrambled response approach to estimate the population mean of the sensitive variables. Math Model Numer Simul Appl. 202210;2(3):127–146.
   Google Scholar
• Barabesi L, Diana G, Perri PF. Design-based distribution function estimation for stigmatized populations. Metrika. 2013;76:7919–935. doi: 10.1007/s00184-012-0424-6
   Web of Science ®Google Scholar
• Barabesi L, Diana G, Perri PF. Horvitz-Thompson estimation with randomized response and nonresponse. Model Assist Stat Appl. 20141;9(1):3–10.
   Google Scholar
• Saha A. A randomized response technique for quantitative data under unequal probability sampling. J Stat Theory Pract. 2008;2(4):589–596. doi: 10.1080/15598608.2008.10411897
   Google Scholar
• Clark JP, Tifft LL. Polygraph and interview validation of self-reported deviant behavior. Am Sociol Rev. 1966;31:4516–23. doi: 10.2307/2090775
   PubMed Web of Science ®Google Scholar
• Jones EE, Sigall H. The bogus pipeline: a new paradigm for measuring affect and attitude. Psychol Bull. 1971;76(5):349.364 doi: 10.1037/h0031617
   Google Scholar
• Roese NJ, Jamieson DW. Twenty years of bogus pipeline research: a critical review and meta-analysis. Psychol Bull. 1993;114(2):363.375 doi: 10.1037/0033-2909.114.2.363
   Web of Science ®Google Scholar
• Tourangeau R, Smith TW, Rasinski KA. Motivation to report sensitive behaviors on surveys: evidence from a bogus pipeline experiment 1. J Appl Soc Psychol. 1997;27(3):209–222. doi: 10.1111/jasp.1997.27.issue-3
   Web of Science ®Google Scholar
• Bauman KE, Dent CW. Influence of an objective measure on self-reports of behavior. J Appl Psychol. 1982;67(5):623.628 doi: 10.1037/0021-9010.67.5.623
   PubMed Web of Science ®Google Scholar
• Chow LP, Rider RV. The randomized response technique as used in the Taiwan outcome of pregnancy study. Stud Fam Plann. 1972;3(11):265–9. doi: 10.2307/1965247
   Web of Science ®Google Scholar
• Bova CS, Halse SJ, Aswani S, et al. Assessing a social norms approach for improving recreational fisheries compliance. Fish Manag Ecol. 2017;24(2):117–125. doi: 10.1111/fme.2017.24.issue-2
   Web of Science ®Google Scholar
• Van der Heijden PG, Van Gils G, Bouts J, et al. 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. Sociol Methods Res. 2000;28(4):505–537. doi: 10.1177/0049124100028004005
   Web of Science ®Google Scholar
• Moshagen M, Hilbig BE, Erdfelder E, et al. An experimental validation method for questioning techniques that assess sensitive issues. Exper Psychol. 2014;61(1):48.54 doi: 10.1027/1618-3169/a000226
   PubMed Web of Science ®Google Scholar
• Fox JA, Tracy PE. Randomized response: a method for sensitive surveys. Beverly Hills: Sage, Quantitative Applications in the Social Sciences; 1986.
   Google Scholar
• Yu JW, Tian GL, Tang ML. Two new models for survey sampling with sensitive characteristic: design and analysis. Metrika. 2008;67(3):251–263. doi: 10.1007/s00184-007-0131-x
   Web of Science ®Google Scholar
• Gavin MC, Solomon JN, Blank SG. Measuring and monitoring illegal use of natural resources. Conserv Biol. 2010;24(1):89–100. doi: 10.1111/j.1523-1739.2009.01387.x
   PubMed Web of Science ®Google Scholar
• Junger-Tas J, Marshall IH. The self-report methodology in crime research. Crime and Justice. 1999;25:291–367. doi: 10.1086/449291
   Web of Science ®Google Scholar
• Ahart AM, Sackett PR. A new method of examining relationships between individual difference measures and sensitive behavior criteria: evaluating the unmatched count technique. Organ Res Methods. 2004;7(1):101–114. doi: 10.1177/1094428103259557
   Web of Science ®Google Scholar
• Dalton DR, Wimbush JC, Daily CM. Using the unmatched count technique (UCT) to estimate base rates for sensitive behavior. Pers Psychol. 1994;47(4):817–829. doi: 10.1111/peps.1994.47.issue-4
   Web of Science ®Google Scholar
• Trappmann M, Krumpal I, Kirchner A, et al. Item sum: a new technique for asking quantitative sensitive questions. J Surv Stat Methodol. 2014;2(1):58–77. doi: 10.1093/jssam/smt019
   Google Scholar
• Hussain Z, Shabbir N, Shabbir J. An alternative item sum technique for improved estimators of population mean in sensitive surveys. Hacet J Math Stat. 2017;46(5):907–34.
   Web of Science ®Google Scholar
• Krumpal I, Jann B, Korndörfer M, et al. Item sum double-list technique: an enhanced design for asking quantitative sensitive questions. In Survey Research Methods. European Survey Research Association. 12(2):2018. p. 91–102.
   Google Scholar
• Hoffmann A, Meisters J, Musch J. On the validity of non-randomized response techniques: an experimental comparison of the crosswise model and the triangular model. Behav Res Methods. 2020;52(4):1768–1782. doi: 10.3758/s13428-020-01349-9
   PubMed Web of Science ®Google Scholar
• Meisters J, Hoffmann A, Musch J. Can detailed instructions and comprehension checks increase the validity of crosswise model estimates?. PLoS ONE. 2020;15(6):e0235403. doi: 10.1371/journal.pone.0235403
   PubMed Web of Science ®Google Scholar
• Meisters J, Hoffmann A, Musch J. Controlling social desirability bias: an experimental investigation of the extended crosswise model. PLoS ONE. 2020;15(12):e0243384. doi: 10.1371/journal.pone.0243384
   PubMed Web of Science ®Google Scholar
• Meisters J, Hoffmann A, Musch J. A new approach to detecting cheating in sensitive surveys: the cheating detection triangular model. Sociol Methods Res. 2022;19:00491241211055764.
   Google Scholar
• Meisters J, Hoffmann A, Musch J. More than random responding: empirical evidence for the validity of the (Extended) crosswise model. Behav Res Methods. 2023;55(2):716–729. doi: 10.3758/s13428-022-01819-2
   PubMed Web of Science ®Google Scholar
• R Core Team. R: a language and environment for statistical computing. R Found. Stat. Comput., Vienna, Austria. 2018. Available from: https://www.R-project.org/.
   Google Scholar
• Moriarty M, Wiseman F. On the choice of a randomization technique with the randomized response model. In: Proceedings of the Social Statistics Section. American Statistical Association; 1976. p. 624–626.
   Google Scholar
• Diekmann A. Making use of ‘Benford's law’ for the randomized response technique. Sociol Methods Res. 2012;41(2):325–334. doi: 10.1177/0049124112452525
   Web of Science ®Google Scholar
• Kundt T. Applying ‘Benford's law’ to the crosswise model: findings from an online survey on tax evasion. Diskussionspapier, No. 148, Helmut-SchmidtUniversitt – Universitt der Bundeswehr Hamburg, Fchergruppe Volkswirtschaftslehre, Hamburg. Available at SSRN 2487069. 2014 Jul 29. Available from: https://www.econstor.eu/bitstream/10419/102311/1/790420716.pdf.
   Google Scholar
• Siegrist K. 5.39: Benford's law. Statistics LibreTexts. 2022 April 24. Available from: https://stats.libretexts.org/Bookshelves/Probability_Theory/Probability_Mathematical_Statistics_and_Stochastic_Processes_(Siegrist)/05%3A_Special_Distributions/5.39%3A_Benford's_Law.
   Google Scholar
• Himmelfarb S, Edgell SE. Additive constants model: a randomized response technique for eliminating evasiveness to quantitative response questions. Psychol Bull. 1980;87(3):525.530 doi: 10.1037/0033-2909.87.3.525
   Web of Science ®Google Scholar
• Diana G, Giordan M, Perri PF. Randomized response surveys: a note on some privacy protection measures. Model Assist Stat Appl. 2013;8(1):19–28.
   Google Scholar
• Gupta S, Mehta S, Shabbir J, et al. A unified measure of respondent privacy and model efficiency in quantitative RRT models. J Stat Theory Pract. 2018;12(3):506–511. doi: 10.1080/15598608.2017.1415175
   Web of Science ®Google Scholar
• Dua D, Graff C. UCI machine learning repository. School of Information and Computer Science, University of California, Irvine, CA. 2019.Available from: http://archive.ics.uci.edu/ml.
   Google Scholar