Physical and cognitive doping in university students using the unrelated question model (UQM): Assessing the influence of the probability of receiving the sensitive question on prevalence estimation

• Published in: PloS one 2018-05, Vol.13 (5), p.e0197270
• Main Authors: Dietz, Pavel, Quermann, Anne, van Poppel, Mireille Nicoline Maria, Striegel, Heiko, Schröter, Hannes, Ulrich, Rolf, Simon, Perikles
• Format: Article
• Language: English
• Subjects: Analysis; Bayesian analysis; Biology and Life Sciences; Cognitive ability; Cognitive enhancement; College students; Colleges & universities; Disease prevention; Doping; Drug therapy; Estimates; Hypothesis testing; Likelihood ratio; Management; Medicine and Health Sciences; Methods; Physical fitness; Physical Sciences; Public health; Research and Analysis Methods; Social Sciences; Software; Sport science; Sports medicine; Statistical analysis; Stimulants; Students; Studies; Surveys; Triathlon; Universities and colleges; University students
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0197270

In order to increase the value of randomized response techniques (RRTs) as tools for studying sensitive issues, the present study investigated whether the prevalence estimate for a sensitive item [Formula: see text] assessed with the unrelated questionnaire method (UQM) is influenced by changing the probability of receiving the sensitive question p. A short paper-and-pencil questionnaire was distributed to 1.243 university students assessing the 12-month prevalence of physical and cognitive doping using two versions of the UQM with different probabilities for receiving the sensitive question (p ≈ 1/3 and p ≈ 2/3). Likelihood ratio tests were used to assess whether the prevalence estimates for physical and cognitive doping differed significantly between p ≈ 1/3 and p ≈ 2/3. The order of questions (physical doping and cognitive doping) as well as the probability of receiving the sensitive question (p ≈ 1/3 or p ≈ 2/3) were counterbalanced across participants. Statistical power analyses were performed to determine sample size. The prevalence estimate for physical doping with p ≈ 1/3 was 22.5% (95% CI: 10.8-34.1), and 12.8% (95% CI: 7.6-18.0) with p ≈ 2/3. For cognitive doping with p ≈ 1/3, the estimated prevalence was 22.5% (95% CI: 11.0-34.1), whereas it was 18.0% (95% CI: 12.5-23.5) with p ≈ 2/3. Likelihood-ratio tests revealed that prevalence estimates for both physical and cognitive doping, respectively, did not differ significantly under p ≈ 1/3 and p ≈ 2/3 (physical doping: χ2 = 2.25, df = 1, p = 0.13; cognitive doping: χ2 = 0.49, df = 1, p = 0.48). Bayes factors computed with the Savage-Dickey method favored the null ("the prevalence estimates are identical under p ≈ 1/3 and p ≈ 2/3") over the alternative ("the prevalence estimates differ under p ≈ 1/3 and p ≈ 2/3") hypothesis for both physical doping (BF = 2.3) and cognitive doping (BF = 5.3). The present results suggest that prevalence estimates for physical and cognitive doping assessed by the UQM are largely unaffected by the probability for receiving the sensitive question p.