Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Petróczi, Andrea; Mazanov, Jason; Nepusz, Tamás; Backhouse, Susan H; Naughton, Declan P (2008)
Publisher: BioMed Central
Journal: Journal of Occupational Medicine and Toxicology (London, England)
Languages: English
Types: Article
Subjects: Industrial medicine. Industrial hygiene, sports, RC963-969, Research



The 'False Consensus Effect' (FCE), by which people perceive their own actions as relatively common behaviour, might be exploited to gauge whether a person engages in controversial behaviour, such as performance enhancing drug (PED) use.


It is assumed that people's own behaviour, owing to the FCE, affects their estimation of the prevalence of that behaviour. It is further hypothesised that a person's estimate of PED population use is a reliable indicator of the doping behaviour of that person, in lieu of self-reports.

Testing the hypothesis

Over- or underestimation is calculated from investigating known groups (i.e. users vs. non-users), using a short questionnaire, and a known prevalence rate from official reports or sample evidence. It is proposed that sample evidence from self-reported behaviour should be verified using objective biochemical analyses.

In order to find proofs of concept for the existence of false consensus, a pilot study was conducted. Data were collected among competitive UK student-athletes (n = 124) using a web-based anonymous questionnaire. User (n = 9) vs. non-user (n = 76) groups were established using self-reported information on doping use and intention to use PEDs in hypothetical situations. Observed differences in the mean estimation of doping made by the user group exceeded the estimation made by the non-user group (35.11% vs. 15.34% for general doping and 34.25% vs. 26.30% in hypothetical situations, respectively), thus providing preliminary evidence in support of the FCE concept in relation to doping.

Implications of the hypothesis

The presence of the FCE in estimating doping prevalence or behaviour in others suggests that the FCE based approach may be an avenue for developing an indirect self-report mechanism for PED use behaviour. The method may be successfully adapted to the estimation of prevalence of behaviours where direct self-reports are assumed to be distorted by socially desirable responding. Thus this method can enhance available information on socially undesirable, health compromising behaviour (i.e. PED use) for policy makers and healthcare professionals. The importance of the method lies in its usefulness in epidemiological studies, not in individual assessments.

  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Kayser B, Mauron A, Miah A: Current anti-doping policy: A critical appraisal. BMC Medical Ethics 2007, 8:2.
    • Yesalis CE, Kopstein AN, Bahrke MS: Difficulties in estimating the prevalence of drug use among athletes. In Doping in elite sport: The politics of drugs in the Olympic movement Edited by: Wilson W, Derse E. Human Kinetics; 2001:43-62.
    • WADA Annual Reports between 2002 - 2006 [http:// www.wada-ama.org/en/dynamic.ch2?pageCategory.id=453]
    • 4. Waddington I: Changing patterns of drug use in British sport from the 1960s. Sport History 2005, 25:472-496.
    • 5. Morgan WJ: Fair is fair, or is it?: A moral consideration of the doping wars in American sport. Sport Society 2006, 9:177-198.
    • 6. Laure P: Epidemiologic approach of doping in sport. J Sports Med Phys Fitness 1997, 37(3):218-224.
    • 7. Papadopoulos F, Skalkidis I, Parkkari J, Petridou E, "Sports Injuries" European Union Group: Doping use among tertiary education students in six developed countries. Europ J Epidemiol 2006, 21:307-313.
    • 8. Trout GJ, Kazlauskas R: Sports drug testing - an analyst's perspective. Chem Soc Rev 2004, 33:1-13.
    • 9. Lara D, Garcia SG, Ellertson C, Camlin C, Suarez J: The measure of induced abortion levels in Mexico using random response technique. Soc Methods Res 2006, 35:279-301.
    • 10. Nishimura YH, Ono-Kihara M, Mohithm JC, Ng Man Sun R, Homma T, DiClemente RJ, Lang DL, Kihara M: Sexual behaviors and their correlates among young people in Mauritius: a cross-sectional study. BMC Int Health Human Rights 2007, 7:8.
    • 11. Lensvelt-Mulders GJLM, Hox JJ, Heijden PGM van der, Maas CJM: Meta-analysis of randomised response research. Soc Methods Res 2005, 33:319-348.
    • 12. Simon P, Striegel H, Aust F, Dietz K, Ulrich R: Doping in fitness sports: estimated number of underreported cases and individual probability of doping. Addition 2006, 101:1640-1644.
    • 13. Allport FH: Social Psychology Cambridge, MA: Riverside Press; 1924.
    • 14. Agostinelli G, Seal DW: Social comparison of one's own with others attitudes towards causal and responsible sex. J Appl Soc Psychol 1988, 28:845-860.
    • 15. Buunk BP, Kluwer ES, Schuurman MK, Siero FW: The division of labor among egalitarian and traditional women: differences in discontent, social comparison and false consensus. J Appl Soc Psychol 2000, 30:759-779.
    • 16. Buunk BP, Eijden RJJ van den, Siero FW: The double-edged sword of providing information about the prevalence of safe sex. J Appl Soc Psychol 2002, 32:684-699.
    • 17. Monin B, Norton MI: Perceptions of a fluid consensus: uniqueness bias, false consensus, false polarization, and pluralistic ignorance in a water conservation crisis. Personal Soc Psychol Bull 2003, 29:559-567.
    • 18. Suls J, Wan CK, Sanders GS: False consensus and false uniqueness in estimating the prevalence of health-protective behaviours. J Appl Soc Psychol 1988, 18:66-79.
    • 19. Wolfson S: Students' estimates of the prevalence of drug use: evidence for a false consensus effect. Psychol Addict Behav 2000, 14:295-298.
    • 20. Holmes DS: Dimensions of projection. Psychol Bullet 1968, 69:248-268.
    • 21. Ross L, Greene D, House P: The false consensus effect: An egocentric bias in social perception and attribution processes. J Exp Socl Psychol 1977, 13:279-301.
    • 22. Krueger J, Clement RW: The truly false consensus effect: an ineradicable and egocentric bias in social perception. J Pers Soc Psychol 1994, 67:596-610. [Erratum in: J Pers Soc Psychol 1995, 68:579].
    • 23. Gershoff AD, Mukherjee A, Mukhopadhyay A: What's not to like? Preference asymmetry in the false consensus effect. J Consumer Res 2007, 31(1):119-125.
    • 24. Juvonen J, Martino SC, Ellickson PL, Longshore D: But others do it!": Do misperception of schoolmate alcohol and marijuana use predict subsequent drug use among young adolescents. J Appl Soc Psychol 2007, 37:740-758.
    • 25. Lai MK, Ho SY, Lam TH: Perceived peer smoking prevalence and its association with smoking behaviours and intentions in Hong Kong Chinese adolescents. Addiction 2007, 99:1195-1205.
    • 26. McCabe SE: Misperceptions of non-medical prescription drug use: a web-survey of college students. Addict Behav 2008, 33:713-724.
    • 27. Pearson B, Hansen B: Survey of U.S. Olympians. USA Today. February 5, 1992, 10C.
    • 28. Alaranta A, Alaranta H, Holmila J, Palmu P, Pietila K, Helenius I: Selfreported attitudes of elite athletes towards doping: differences between type of sport. Int J Sport Med 2006, 27:842-846.
    • 29. Backhouse S, McKenna J, Robinson S, Atkin A: Attitudes, Behaviours, Knowledge and Education - Drugs in Sport: Past, Present and Future 2007. [http://www.wada-ama.org].
    • 30. Ashraf W, Jaffar M, Anwer K, Ehsan U: Age- and sex-based comparative distribution of selected metals in the scalp hair of an urban population from two cities in Pakistan. Environ Pollut 1995, 87:61-64.
    • 31. Pragst F, Balikova MA: State of the art in hair analysis for detection of drug and alcohol abuse. Clin Chim Acta 2006, 370:17-49.
    • 32. Forte G, Alimonti A, Violante N, Di Gregorio M, Senofonte O, Petrucci F, Giuseppe Sancesario G, Bocca B: Calcium, copper, iron, magnesium, silicon and zinc content of hair in Parkinson's disease. J Trace Elem Exp Med 2005, 19:195-201.
    • 33. Rao KS, Balaji T, Rao TP, Babu Y, Naidu GRK: Determination of iron, cobalt, nickel, manganese, zinc, copper, cadmium and lead in human hair by inductively coupled plasma atomic emission spectrometry. Spectrochim Acta Part 2002, 57:1333-1338.
    • 34. Pujol ML, Cirimele V, Tritsch P, Villain M, Kintz P: Evaluation of the IDS One-StepTM ELISA kits for the detection of illicit drugs in hair. Forensic Sci Int 2007, 170:189-192.
    • 35. Dumestre-Toulet V, Cirimele V, Ludes B, Gromb S, Kintz P: Hair analysis of seven bodybuilders for anabolic steroids, ephedrine and clenbuterol. J Forensic Sci 2002, 47:211-214.
    • 36. Petroczi A, Naughton DP, Mazanov J, Holloway A, Bingham J: Performance enhancement with supplements: incongruence between rationale and practice. J Int Soc Sports Nutr 2007, 4:19.
    • 37. Petroczi A, Naughton DP, Mazanov J, Holloway A, Bingham J: Limited agreement exists between rationale and practice in athletes' supplement use for maintenance of health: a retrospective study. Nutr J 2007, 6:34.
    • 38. Cunningham JA, Selby PL: Implications of the normative fallacy in young adult smokers aged 19-24 Years. Am J Pub Health 2007, 97:1399-1400.
    • 39. Petroczi A, Aidman EV: Psychological drivers in doping: the lifecycle model of performance enhancement. Subst Abuse Treat Prev Policy 2008, 3:7.
  • No related research data.
  • No similar publications.