The analysis of randomized response “ever” and “last year” questions: A non-saturated Multinomial model

Randomized response (RR) is a well-known interview technique designed to eliminate evasive response bias that arises from asking sensitive questions. The most frequently asked questions in RR are either whether respondents were “ever” carriers of the sensitive characteristic, or whether they were carriers in a recent period, for instance, “last year”. The present paper proposes a design in which both questions are asked, and derives a multinomial model for the joint analysis of these two questions. Compared to the separate analyses with the binomial model, the model makes a useful distinction between last year and former carriers of the sensitive characteristic, it is more efficient in estimating the prevalence of last year carriers, and it has a degree of freedom that allows for a goodness-of-fit test. Furthermore, it is easily extended to a multinomial logistic regression model to investigate the effects of covariates on the prevalence estimates. These benefits are illustrated in two studies on the use of anabolic androgenic steroids in the Netherlands, one using Kuk and one using both the Kuk and forced response. A salient result of our analyses is that the multinomial model provided ample evidence of response biases in the forced response condition

Estimating the prevalence of crack dependence using capture-recapture with institutional and field data:A three-city study in the Netherlands

The aim of this study was to estimate the prevalence of crack dependence in the three largest Dutch cities (Amsterdam, Rotterdam, The Hague), stratified by gender and age. Three-sample capture-recapture, using data (collected between 2009 and 2011) from low threshold substitution treatment (n = 1,764), user rooms (n = 546), and a respondent-driven sample (n = 549), and applying log-linear modeling (covariates: gender, age, and city), provided a prevalence rate of 0.51% (95% CI: 0.46%-0.60%) for the population aged 15-64 years, with similar estimates for the three cities. Females (23.0% of total estimate) and younger crack users (12.8% aged &lt;35 years) might be underrepresented in drug user treatment services.</p

Hidden figures: Revisiting doping prevalence estimates previously reported for two major international sport events in the context of further empirical evidence and the extant literature

High levels of admitted doping use (43.6% and 57.1%) were reported for two international sport events in 2011 in Ulrich et al., 2018. Because these are frequently referenced in evaluating aspects of anti-doping, having high level of confidence in these estimates is paramount. In this study, we present new prevalence estimates from a concurrently administered method, the Single Sample Count (SSC), and critically review the two sets of estimates in the context of other doping prevalence estimates. Estimates with the SSC model for 12-month doping prevalence were lower than previously reported : 21.2% (95%CI: 9.69–32.7) at WCA and 10.6% (95%CI: 1.76–19.4) at PAG. Estimated herbal, mineral, and/or vitamin supplements use was 8.57% (95%CI: 1.3-16.11) at PAG. Caution in interpreting these estimates as bona fide prevalence rates is warranted. Noncompliance appears to be the Achilles heel of the indirect estimation models thus it should be routinely tested for and minimized. Further research into cognitive and behaviour aspects, including motivation for honesty, is needed to improve the ecological validity of the estimated prevalence rates

Image and performance enhancing drugs, image-centric social media use, and body image in male gym users

Male gym users participated in an online questionnaire including self-reported measures regarding resistance training, image-centric social media use, dietary supplement intake, and body image. Prevalence of AAS and SARM was assessed with randomized responses, a technique to ask sensitive questions indirectly

Refinement of the extended crosswise model with a number sequence randomizer: evidence from three different studies in the UK

The Extended Crosswise Model (ECWM) is a randomized response model with neutral response categories, relatively simple instructions, and the availability of a goodness-of-fit test. This paper refines this model with a number sequence randomizer that virtually precludes the possibility to give evasive responses. The motivation for developing this model stems from a strategic priority of WADA (World Anti-Doping Agency) to monitor the prevalence of doping use by elite athletes. For this model we derived a maximum likelihood estimator that allows for binary logistic regression analysis. Three studies were conducted on online platforms with a total of over 6, 000 respondents; two on controlled substance use and one on compliance with COVID-19 regulations in the UK during the first lockdown. The results of these studies are promising. The goodness-of-fit tests showed little to no evidence for response biases, and the ECWM yielded higher prevalence estimates than direct questions for sensitive questions, and similar ones for non-sensitive questions. Furthermore, the randomizer with the shortest number sequences yielded the smallest response error rates on a control question with known prevalence