New non-randomised model to assess the prevalence of discriminating behaviour: a pilot study on mephedrone

Substance Abuse Treatment, Prevention, and Policy

Declan P Naughton³  James Barker⁶  Christiana Adesanwo³  Maryann Shane¹  Jay Schaffer¹  Nawed Deshmukh⁶  Syeda Shah⁶  Helen Taft⁴  Paul Cross⁴  Tamás Nepusz⁵  Andrea Petróczi² 

[1] Applied Statistics and Research Methods, University of Northern Colorado, USA;Department of Psychology, University of Sheffield, UK;School of Life Sciences, Kingston University, UK;School of Pharmacy and Chemistry, Kingston University, UK;Department of Biological Physics, Eötvös Loránd University, Hungary;School of the Environment, Natural Resources and Geography, Bangor University, UK

关键词: epidemiology;    illicit substances;    survey;    Mephedrone;    non-random model;    random response technique;   

Others  :  834577
DOI  :  10.1186/1747-597X-6-20

 received in 2011-05-08, accepted in 2011-08-03,  发布年份 2011

【 摘 要 】

Background

An advantage of randomised response and non-randomised models investigating sensitive issues arises from the characteristic that individual answers about discriminating behaviour cannot be linked to the individuals. This study proposed a new fuzzy response model coined 'Single Sample Count' (SSC) to estimate prevalence of discriminating or embarrassing behaviour in epidemiologic studies.

Methods

The SSC was tested and compared to the established Forced Response (FR) model estimating Mephedrone use. Estimations from both SSC and FR were then corroborated with qualitative hair screening data. Volunteers (n = 318, mean age = 22.69 ± 5.87, 59.1% male) in a rural area in north Wales and a metropolitan area in England completed a questionnaire containing the SSC and FR in alternating order, and four questions canvassing opinions and beliefs regarding Mephedrone. Hair samples were screened for Mephedrone using a qualitative Liquid Chromatography-Mass Spectrometry method.

Results

The SSC algorithm improves upon the existing item count techniques by utilizing known population distributions and embeds the sensitive question among four unrelated innocuous questions with binomial distribution. Respondents are only asked to indicate how many without revealing which ones are true. The two probability models yielded similar estimates with the FR being between 2.6% - 15.0%; whereas the new SSC ranged between 0% - 10%. The six positive hair samples indicated that the prevalence rate in the sample was at least 4%. The close proximity of these estimates provides evidence to support the validity of the new SSC model. Using simulations, the recommended sample sizes as the function of the statistical power and expected prevalence rate were calculated.

Conclusion

The main advantages of the SSC over other indirect methods are: simple administration, completion and calculation, maximum use of the data and good face validity for all respondents. Owing to the key feature that respondents are not required to answer the sensitive question directly, coupled with the absence of forced response or obvious self-protective response strategy, the SSC has the potential to cut across self-protective barriers more effectively than other estimation models. This elegantly simple, quick and effective method can be successfully employed in public health research investigating compromising behaviours.

【 授权许可】

   
2011 Petróczi et al; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Pan W, Bai H: A Multivariate Approach to a Meta-Analytic Review of the Effectiveness of the D.A.R.E. Program. Int J Environ Res Public Health 2009, 6(1):267-277.
• [2]Snyder LB: Health Communication Campaigns and their Impact on Behavior. J Nutr Educ Behav 2007, 39(25):S32-S39.
• [3]Keller PA, Lehman DR: Designing effective health communication: a meta-analysis. J Public Policy Mark 2008, 27(2):1-26.
• [4]Merzel C, D'Afflitti J: Reconsidering community-based health promotion: promise, performance, and potential. Am J Public Health 2003, 93:557-574.
• [5]Robling M, Ingledew DK, Greene G, Sayers A, Shaw C, Sander L, Russell IT, Williams JG, Hood K: Applying an extended theoretical framework for data collection mode to health services research. BMC Health Serv Res 2010, 10:180. BioMed Central Full Text
• [6]Tourangeau R, Yan T: Sensitive questions in surveys. Psychol Bull 2007, 133:859-883.
• [7]Petróczi A, Aidman EV, Hussain I, Deshmukh N, Nepusz T, Uvacsek M, Tóth M, Barker J, Naughton DP: Virtue or pretense? Looking behind self-declared innocence in doping. PLoS One 2010, 5(5):e10457.
• [8]Petróczi A, Uvacsek M, Deshmukh N, Shah I, Nepusz T, Aidman EV, Tóth M, Barker J, Naughton DP: Incongruence in doping related attitudes, beliefs and opinions in the context of discordant behavioural data: In which measure do we trust? PLoS One 2011, 6(4):e18804.
• [9]Petróczi A, Nepusz T: Methodological considerations regarding response bias effect in substance use research: is correlation between the measured variables sufficient? Subs Abuse Treat Prev Policy 2011, 6:1. BioMed Central Full Text
• [10]Jones E, Sigall H: The Bogus Pipeline: A new paradigm for measuring affect and attitude. Psychol Bull 1971, 76(5):349-364.
• [11]Prelec D: A Bayesian Truth Serum for subjective data. Science 2004, 306:462-466.
• [12]Roese NJ, Jamieson DW: Twenty years of bogus pipeline research: a critical review and meta-analysis. Psychol Bull 1993, 114:363-375.
• [13]Barrage L, Lee MS: A penny for your thoughts: inducing truth telling in stated preference elicitation. Economics Letters 2010, 106:140-142.
• [14]Offerman T, Sonnemans J, van de Kuilen , Wakker PP: Truth Serum for non-Bayesians: correcting proper scoring rules for risk attitudes. Rev Econ Studies 2009, 76(4):1461-1489.
• [15]Lensvelt-Mulders GJLM, Hox JJ, van der Heijden PGM: Meta-analysis of randomized response research. Thirty-five years of validation. Sociol Methods Res 2005, 33:319-347.
• [16]Warner SL: Randomized response: A survey technique for eliminating evasive answer bias. J Am Stat Assoc 1965, 60:63-69.
• [17]Lensvelt-Mulders GJLM, Hox JJ, van der Heijden PGM, Maas CJM: How to improve efficiency of randomised response designs. Qual Quant 2005, 39:253-265.
• [18]Himmelfarb S: The multi-item randomized response technique. Sociol Methods Res 2008, 36:495-514.
• [19]Fox J-P, Wyrick C: A mixed effects randomized item response model. J Educ Behav Stat 2008, 33:389-414.
• [20]Droitcour J, Caspar RA, Hubbard ML, Parsely TL, Vicer W, Ezzati TM: The item count technique as a method of indirect questioning: a review of its development and a case study application. In Measurement errors in surveys. Edited by Biemer P, Groves RM, Lyberg LE, Mathiowetz NA, Sudman S. New York: Wiley; 1991:185-210.
• [21]Ming T-T, Tian G-L, Tang M-L: Sample surveys with sensitive questions: a nonrandomized response approach. Am Stat 2009, 63(1):9-16.
• [22]Miller JD: A new survey technique for studying deviant behavior. PhD thesis. The George Washington University; 1984.
• [23]Dalton DR, Wimbush JC, Daily CM: Using the Unmatched List Technique (UCT) to estimate base rates for sensitive behavior. Pers Psychol 1994, 47:817-828.
• [24]Horvitz DG, Shah BV, Simmons WR: The unrelated question randomized response model. Social Stat Sect Proc Am Stat Assoc 1967, 65-72.
• [25]Yu JW, Tian G-L, Tang M-L: Two new models for survey sampling with sensitive characteristic: design and analysis. Metrika 2008, 67:251-263.
• [26]Tian G-L, Yu J-W, Tang M-L, Geng Z: A new non-randomized model for analyzing sensitive questions with binary outcomes. Stat Med 2007, 26:4238-4252.
• [27]Tian G-L, ChuenYuen K, Tang M-L, Tan MT: Bayesian non-randomized response models for surveys with sensitive questions. Stat and Interface 2009, 2:13-25.
• [28]Coutts E, Jann B: Sensitive questions in online surveys: experimental results for the Randomized Response Technique (RRT) and the Unmatched Count Technique (UCT). Sociol Method Res 2011, 40:169-193.
• [29]Tsuchiya T: Domain estimators for the item count technique. Surv Methodol 2005, 31:41-51.
• [30]Tsuchiya T, Hirai Y, Ono S: A study of the properties of the item count technique. Public Opin Quart 2007, 71:253-272.
• [31]Tian G-L, Tang M-L, Liu Z, Ming Tan M, Tang N-S: Sample size determination for the non-randomised triangular model for sensitive questions in a survey. Stat Methods Med Res 2009.
• [32]Warner SL: The linear randomized response model. J Am Stat Assoc 1971, 66:884-888.
• [33]Vardakou I, Pistos C, Spiliopoulou Ch: Drugs for youth via Internet and the example of Mephedrone. Toxicol Lett 2011, 201(3):191-195.
• [34]Home Office UK: A change to the Misuse of Drugs Act 1971: Control of mephedrone and other cathinone derivatives. Circular (010/2010) Misuse of Drugs Act 1971 (amendment) order 2010 (S.I. 2010/1207): Misuse of Drugs (designation) (amendment) (England, Wales and Scotland) order 2010 (s.i. 2010/1143); Misuse of Drugs (amendment) (England, Wales and Scotland) regulations 2010 (S.I. 2010/1144)
• [35]Morris K: UK places generic ban on Mephedrone drug family. Lancet 2010, 375:1333-1334.
• [36]Winstock A, Mitcheson L, Marsden J: Mephedrone: still available and twice the price. Lancet 2010, 376:1537.
• [37]Brandt SD, Sumnall HR, Measham F, Cole J: Analyses of second-generation 'legal highs' in the UK: Initial findings. Drug Test Anal 2010, 2:337-382.
• [38]Petroczi A, Naughton D: Impact of multidisciplinary research on advancing anti-doping efforts. Int J Sport Policy 2011, 3:231-254.
• [39]Boruch R: Assuring confidentiality of responses in educational research: A note on strategies. Am Sociologist 1971, 6:308-311.
• [40]Drug Misuse Declared: Findings from the 2007/08 British Crime Survey. England and Wales Home Office Statistical Bulletin; 2008. ISSN 1358-510X
• [41]Drug Misuse Declared: Findings from the 2008/09 British Crime Survey. England and Wales Home Office Statistical Bulletin; 2009. ISSN 1358-510X
• [42]Drug Misuse Declared: Findings from the 2009/10 British Crime Survey. England and Wales Home Office Statistical Bulletin; 2010. ISSN 1358-510X
• [43]Ross L, Greene D, House P: The false consensus effect: An egocentric bias in social perception and attribution processes. J Experiment Soc Psychol 1977, 13:279-301.
• [44]Borsari B, Carey KB: Descriptive and injunctive norms in college drinking: a meta-analytic integration. J Stud Alcohol 2003, 64(3):331-341.
• [45]LaBrie JW, Grossbard JR, Hummer JF: Normative misperceptions and marijuana use among male and female college athletes. J Appl Sport Psychol 2009, 21(Supp 1):S77-S85.
• [46]Petróczi A, Aidman EV, Hussain I, Deshmukh N, Nepusz T, Uvacsek M, Tóth M, Barker J, Naughton DP: Virtue or pretense? Looking behind self-declared innocence in doping. PLoS One 2010, 5(5):e10457.
• [47]Petróczi A, Uvacsek M, Nepusz T, Deshmukh N, Shah I, Aidman EV, Barker J, Tóth M, Naughton DP: Incongruence in doping related attitudes, beliefs and opinions in the context of discordant behavioural data: in which measure do we trust? PLoS One 2011, 6(4):e18804.
• [48]McCrae RR, Costa PT: Social desirability scales: More substance than style. J Consult Clin Psych 1983, 51:882-888.
• [49]Uziel L: Rethinking social desirability scales: From impression management to interpersonally oriented self-control. Perspect Psychol Sci 2010, 5(3):243-262.
• [50]Lensvelt-Mulders GJLM, Boeije HR: Evaluating compliance with computer assisted randomized response technique: a qualitative study into the origins of lying and cheating. Comput Human Behav 2007, 23:591-608.
• [51]Böckenholt U, Barlas S, van der Heijden PGM: Do randomized-response design eliminate response biases? An empirical study of non-compliance behavior. J Appl Econom 2009, 24:377-392.
• [52]Kuk AYC: Asking sensitive questions indirectly. Biometrika 1990, 77:346-438.
• [53]Ostapczuk M, Musch J, Moshagen M: Improving self-report measures of medication non-adherence using a cheating detection extension of the randomized-response-technique. Stat Methods Med Res 2010.
• [54]Clark SJ, Desharnais RA: Honest answers to embarrassing questions: detecting cheating in the randomized response model. Psychol Methods 1998, 3:160-168.
• [55]Böckenholt U, van der Heijden PGM: Item randomized-response models for measuring noncompliance: risk-return perceptions, social influences and self-protective responses. Psychometrika 2007, 72:245-262.
• [56]van den Hout A, Böckenholt U, van der Heijden PGM: Estimating the prevalence of sensitive behavior and cheating with dual design for direct questioning and randomized response. Appl Stat 2010, 59:723-736.
• [57]Cruyff MJLF, van den Hout A, van der Heijden PGM, Böckenholt U: Log-linear randomized-response models taking self-protective response behavior into account. Sociol Method Res 2007, 36:266-282.
• [58]Cruyff MJLF, Böckenholt U, van den Hout A, van der Heijden PGM: Accounting for self-protective responses in randomized response data from a social security survey using the zero-inflated Poisson model. Ann Appl Stat 2008, 2:136-331.
• [59]Frank LE, van den Hout A, van der Heijden PGM: Repeated cross-sectional randomized response data: Taking design change and self-protective responses into account. Methodology 2009, 5:145-152.
• [60]van den Hout A, Klugkist I: Accounting for non-compliance in the analysis of randomized response data. Aust N Z J Stat 2009, 51:353-372.
• [61]Boeije H, Lensvelt-Mulders G: Honest by chance: a qualitative interview study to clarify respondents' (non-)compliance with computer assisted randomized response. Bull Methodol Sociolog 2002, 75:24-39.
• [62]Buhrmester M, Kwang T, Gosling SD: Amazon's mechanical Turk. A new source of inexpensive, yet high-quality, data? Persp Psychol Sci 2011, 6(1):3-5.
• [63]Sartori G, Agosta S, Zogmaister C, Ferrara SD, Castiello U: How to accurately detect autobiographical events. Psychol Sci 2008, 19:772-780.
• [64]Gregg AP: When vying reveals lying: the Timed Antagonistic Response Alethiometer. Appl Cognitive Psych 2007, 21:621-647.