Estimating the prevalence of socially sensitive behaviors: Attributing guilty and innocent noncompliance with the single sample count method

Psychological Methods

Volumn 19, Issue 3, 2014, Pages 334-355

  Nepusz, Tamás ^a,b   Petróczi, Andrea ^a,c   Naughton, Declan P ^a   Epton, Tracy ^c   Norman, Paul ^c  

^a KINGSTON UNIVERSITY   (United Kingdom)

^b Sixdegrees Ltd   (Hungary)

^c UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Birthday distribution; Cheating detection; Illicit drug; Prevalence estimation; Randomized response

Indexed keywords

ADOLESCENT; DECEPTION; EPIDEMIOLOGY; FEMALE; HUMAN; MALE; PREVALENCE; SELF REPORT; STATISTICS; SUBSTANCE-RELATED DISORDERS; UNITED KINGDOM; YOUNG ADULT;

ADOLESCENT; DECEPTION; EPIDEMIOLOGIC METHODS; FEMALE; GREAT BRITAIN; HUMANS; MALE; PREVALENCE; SELF REPORT; STATISTICS AS TOPIC; SUBSTANCE-RELATED DISORDERS; YOUNG ADULT;

EID: 84925797971     PISSN: 1082989X     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0034961     Document Type: Article

References (55)

1. Akaike, H. (1973). Information theory as an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), Second International Symposium on Information Theory (pp. 267-281). Budapest, Hungary: Akadémiai Kiadó.
2. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723. doi:10.1109/TAC.1974.1100705
3. Akaike, H. (1978). A Bayesian analysis of the minimum AIC procedure. Annals of the Institute of Statistical Mathematics, 30, 9-14.
4. Böckenholt, U., Barlas, S., & van der Heijden, P. G. M. (2009). Do randomized-response designs eliminate response biases? An empirical study of non-compliance behavior. Journal of Applied Econometrics, 24, 377-392. doi:10.1002/jae.1052
5. Böckenholt, U., & van der Heijden, P. G. M. (2007). Item randomizedresponse models for measuring noncompliance: Risk return perceptions, social influences, and self-protective responses. Psychometrika, 72, 245-262. doi:10.1007/s11336-005-1495-y
6. Boeije, H., & Lensvelt-Mulders, G. (2002). Honest by chance: A qualitative interview study to clarify respondents' (non-)compliance with computer-assisted randomized response. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 75, 24-39. doi: 10.1177/075910630207500104
7. Boruch, R. F. (1971). Assuring confidentiality of responses in social research: A note on strategies. American Sociologist, 6, 308-311.
8. 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. doi:10.1016/j.jspi.2006.01.004
9. Chaudhuri, A., & Mukherjee, R. (1987). Randomized response techniques: A review. Statistica Neerlandica, 41, 27-44. doi:10.1111/j.1467-9574.1987.tb01169.x
10. Clark, S. J., & Desharnais, R. A. (1998). Honest answers to embarrassing questions: Detecting cheating in the randomized response model. Psychological Methods, 3, 160-168. doi:10.1037/1082-989X.3.2.160
11. Cruyff, M. J. L. F., Böckenholt, U., van den Hout, A., & van der Heijden, P. G. M. (2008). Accounting for self-protective responses in randomized response data from a social security survey using zero-inflated Poisson model. Annals of Applied Statistics, 2, 316-331. doi:10.1214/07-AOAS135
12. Cruyff, M. J. L. F., van den Hout, A., van der Heijden, P. G. M., & Böckenholt, U. (2007). Log-linear randomized-response models taking self-protective response behavior into account. Sociological Methods & Research, 36, 266-282. doi:10.1177/0049124107301944
13. Dalton, D. R., Wimbush, J. C., & Daily, C. M. (1994). Using the unmatched count technique (UCT) to estimate base rates for sensitive behavior. Personnel Psychology, 47, 817-829. doi:10.1111/j.1744-6570.1994.tb01578.x
14. De Schrijver, A. (2012). Sample survey on sensitive topics: Investigating respondents' understanding and trust in alternative versions of the randomized response technique. Journal of Research Practice, 8, Article M1. Retrieved from http://jrp.icaap.org/index.php/jrp/article/view/277
15. Droitcour, J. A., Caspar, R. A., Hubbard, M. L., Parsley, T. L., Visscher, W., & Ezzati, T. M. (1991). The item count technique as a method of indirect questioning: A review of its development and a case study application. In P. P. Biemer, R. M. Groves, L. E. Lyberg, N. Mathiowetz, & S. Sudman (Eds.), Measurement errors in surveys (pp. 185-210). New York, NY: Wiley.
16. Epton, T., Norman, P., Sheeran, P., Harris, P. R., Webb, T. L., Ciravegna, F.,. Kruger, J. (2013). A theory-based online health behavior intervention for new university students: Study protocol. BMC Public Health, 13, Article 107. doi:10.1186/1471-2458-13-107
17. Hansen, N., Müller, S. D., & Koumoutsakos, P. (2003). Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation, 11, 1-18. doi:10.1162/106365603321828970
18. Himmelfarb, S. (2008). The multi-item randomized response technique. Sociological Methods & Research, 36, 495-514. doi:10.1177/0049124107313900
19. Hussain, Z., Shah, E. A., & Shabbir, J. (2012). An alternative item count technique in sensitive surveys. Revista Colombiana de Estadística, 35, 39-54.
20. Imai, K. (2011). Multivariate regression analysis for the item count technique. Journal of the American Statistical Association, 106, 407-416. doi:10.1198/jasa.2011.ap10415
21. James, R. A., Nepusz, T., Naughton, D. P., & Petróczi, A. (2013). A potential inflating effect in estimation models: Cautionary evidence from comparing performance enhancing drug and herbal hormonal supplement use estimates. Psychology of Sport and Exercise, 14, 84-96. doi:10.1016/j.psychsport.2012.08.003
22. Krosnick, J. A. (1999). Survey research. Annual Review of Psychology, 50, 537-567. doi:10.1146/annurev.psych.50.1.537
23. Landsheer, J. A., van der Heijden, P., & van Gils, G. (1999). Trust and understanding, two psychological aspects of randomized response. Quality & Quantity, 33, 1-12. doi:10.1023/A:1004361819974
24. Lee, Y.-O., & Lee, R. M. (2012). Methodological research on "sensitive" topics: A decade review. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 114, 35-49. doi:10.1177/0759106312437139
25. Lelkes, Y., Krosnick, J. A., Marx, D. M., Judd, C. M., & Park, B. (2012). Complete anonymity compromises the accuracy of self-reports. Journal of Experimental Social Psychology, 48, 1291-1299. doi:10.1016/j.jesp.2012.07.002
26. Lensvelt-Mulders, G. J. L. M., & Boeije, H. R. (2007). Evaluating compliance with a computer assisted randomized response technique: A qualitative study into the origins of lying and cheating. Computers in Human Behavior, 23, 591-608. doi:10.1016/j.chb.2004.11.001
27. Lensvelt-Mulders, G. J. L. M., Hox, J. J., & van der Heijden, P. G. M. (2005). Meta-analysis of randomized response research: Thirty-five years of validation. Sociological Methods & Research, 33, 319-348. doi:10.1177/0049124104268664
28. Lensvelt-Mulders, G. J. L. M., Hox, J. J., van der Heijden, P. G. M., & Maas, C. J. M. (2005). How to improve efficiency of randomised response designs. Quality & Quantity, 39, 253-265. doi:10.1007/s11135-004-0432-3
29. Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
30. Mangat, N. (1994). An improved randomized response strategy. Journal of the Royal Statistical Society, 56, 93-95.
31. Monin, B., & Norton, M. I. (2003). Perceptions of a fluid consensus: Uniqueness bias, false consensus, false polarization, and pluralistic ignorance in a water conservation crisis. Personality and Social Psychology Bulletin, 29, 559-567. doi:10.1177/0146167203029005001
32. Moshagen, M., & Musch, J. (2011). Surveying multiple sensitive attributes using an extension of the randomized-response technique. International Journal of Public Opinion Research, 24, 508-523. doi:10.1093/ijpor/edr034
33. Moshagen, M., Musch, J., & Erdfelder, E. (2012). A stochastic lie detector. Behavior Research Methods, 44, 222-231. doi:10.3758/s13428-011-0144-2
34. Moshagen, M., Musch, J., Ostapczuk, M., & Zhao, Z. (2010). Reducing socially desirable responses in epidemiologic surveys. An extension of the randomized-response technique. Epidemiology, 21, 379-382. doi: 10.1097/EDE.0b013e3181d61dbc
35. Nayak, T. K., & Adeshiyan, S. A. (2009). A unified framework for analysis and comparison of randomized response surveys of binary characteristics. Journal of Statistical Planning and Interference, 139, 2757-2766. doi:10.1016/j.jspi.2008.12.013
36. Ostapczuk, M., Moshagen, M., Zhao, Z., & Musch, J. (2009). Assessing sensitive attributes using the randomized response technique: Evidence for the importance of response symmetry. Journal of Educational and Behavioral Statistics, 34, 267-287. doi:10.3102/1076998609332747
37. Ostapczuk, M., & Musch, J. (2011). Estimating the prevalence of negative attitudes towards people with disability: A comparison of direct questioning, projective questioning and randomised response. Disability and Rehabilitation, 33, 399-411. doi:10.3109/09638288.2010.492067
38. Ostapczuk, M., Musch, J., & Moshagen, M. (2011). Improving self-report measures of medication non-adherence using a cheating detection extension of the randomised-response-technique. Statistical Methods in Medical Research, 20, 489-503. doi:10.1177/0962280210372843
39. Parker, H., Williams, L., & Aldridge, J. (2002). The normalization of "sensible" recreational drug use: Further evidence from the North West England Longitudinal Study. Sociology, 36, 941-964. doi:10.1177/003803850203600408
40. Peeters, C. F. W., Lensvelt-Mulders, G. J. L. M., & Lasthuizen, K. (2010). A note on a simple and practical randomized framework for eliciting sensitive dichotomous and qualitative information. Sociological Methods & Research, 39, 283-296. doi:10.1177/0049124110378099
41. Petróczi, A., Cross, P., Taft, H., Shah, S., Deshmukh, N., Nepusz, T.,. Naughton, D. P. (2011). New non-randomised model to assess the prevalence of discriminating behaviour: A pilot study on mephedrone. Substance Abuse Treatment, Prevention, and Policy, 6, Article 20. doi:10.1186/1747-597X-6-20
42. Petróczi, A., & Haugen, K. K. (2012). The doping self-reporting game: The paradox of a "false-telling" mechanism and its potential research and policy implications. Sports Management Review, 15, 513-517. doi: 10.1016/j.smr.2012.04.002
43. Pitsch, W., & Emrich, E. (2012). The frequency of doping in elite sport: Results of a replication study. International Review for the Sociology of Sport, 47, 559-580. doi:10.1177/1012690211413969
44. Raghavarao, D., & Federer, W. T. (1979). Block total response as an alternative to the randomized response method in surveys. Journal of the Royal Statistical Society: Series B (Methodological), 41, 40-45.
45. Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464. doi:10.1214/aos/1176344136
46. SurveyGizmo [Computer software]. (2012). Retrieved from http://www.surveygizmo.com/
47. Tan, M. T., Tian, G. L., & Tang, M. L. (2009). Sample surveys with sensitive questions: A nonrandomized response approach. American Statistician, 63, 9-16. doi:10.1198/tast.2009.0002
48. Tourangeau, R., & Yan, T. (2007). Sensitive questions in surveys. Psychological Bulletin, 133, 859-883. doi:10.1037/0033-2909.133.5.859
49. UK Focal Point on Drugs. (2011). United Kingdom drug situation: 2011 edition. Retrieved from http://www.drugscope.org.uk/Resources/Drugscope/Documents/PDF/Good%20Practice/UKFocalPoint2011.pdf
50. Ulrich, R., Schröter, H., Striegel, H., & Simon, P. (2012). Asking sensitive questions: A statistical power analysis of randomized response models. Psychological Methods, 17, 623-641. doi:10.1037/a0029314
51. van den Hout, A., Böckenholt, U., & van der Heijden, P. G. M. (2010). Estimating the prevalence of sensitive behaviour and cheating with a dual design for direct questioning and randomized response. Journal of the Royal Statistical Society: Series C (Applied Statistics), 59, 723-736.
52. van den Hout, A., & Klugkist, I. (2009). Accounting for non-compliance in the analysis of randomized response data. Australian & New Zealand Journal of Statistics, 51, 353-372. doi:10.1111/j.1467-842X.2009.00552.x
53. van den Hout, A., van der Heijden, P. G. M., & Gilchrist, R. (2007). The logistic regression model with response variables subject to randomized response. Computational Statistics & Data Analysis, 51, 6060-6069. doi:10.1016/j.csda.2006.12.002
54. van der Heijden, P. G. M., van Gils, G., Bouts, J., & Hox, J. J. (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 & Research, 28, 505-537. doi:10.1177/0049124100028004005
55. Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63-69. doi:10.1080/01621459.1965.10480775