Social desirability and the fear of sanctions can deter survey respondents from responding truthfully to sensitive questions. Self-reports on norm breaking behavior such as shoplifting, non-voting, or tax evasion may thus be subject to considerable misreporting. To mitigate such response bias, various indirect question techniques, such as the randomized response technique (RRT), have been proposed. We evaluate the viability of several popular variants of the RRT, including the recently proposed crosswise-model RRT, by comparing respondents’ self-reports on cheating in dice games to actual cheating behavior, thereby distinguishing between false negatives (underreporting) and false positives (overreporting). The study has been implemented as an online survey on Amazon Mechanical Turk (N = 6, 505). Our results from two validation designs indicate that the forced-response RRT and the unrelated-question RRT, as implemented in our survey, fail to reduce the level of misreporting compared to conventional direct questioning. For the crosswise-model RRT we do observe a reduction of false negatives. At the same time, however, there is a non-ignorable increase in false positives; a flaw that previous evaluation studies relying on comparative or aggregate-level validation could not detect. Overall, none of the evaluated indirect techniques outperformed conventional direct questioning. Furthermore, our study demonstrates the importance of identifying false negatives as well as false positives to avoid false conclusions about the validity of indirect sensitive question techniques.

Background Digital proximity tracing apps have been released to mitigate the transmission of SARS-CoV-2, the virus known to cause COVID-19. However, it remains unclear how the acceptance and uptake of these apps can be improved. Objective This study aimed to investigate the coverage of the SwissCovid app and the reasons for its nonuse in Switzerland during a period of increasing incidence of COVID-19 cases. Methods We collected data between September 28 and October 8, 2020, via a nationwide online panel survey (COVID-19 Social Monitor, N=1511). We examined sociodemographic and behavioral factors associated with app use by using multivariable logistic regression, whereas reasons for app nonuse were analyzed descriptively. Results Overall, 46.5% (703/1511) of the survey participants reported they used the SwissCovid app, which was an increase from 43.9% (662/1508) reported in the previous study wave conducted in July 2020. A higher monthly household income (ie, income >CHF 10,000 or >US $11,000 vs income ≤CHF 6000 or <US $6600 [reference]: odds ratio [OR] 1.92, 95% CI 1.40-2.64), more frequent internet use (ie, daily [reference] vs less than weekly: OR 0.37, 95% CI 0.16-0.85), better adherence to recommendations for wearing masks (ie, always or most of the time [reference] vs rarely or never: OR 0.28, 95% CI 0.15-0.52), and nonsmoker status (OR 1.32, 95% CI 1.01-1.71) were associated with an increased likelihood for app uptake. Citizenship status (ie, non-Swiss citizenship vs. Swiss [reference]: OR 0.61, 95% CI 0.43-0.87), and language region (French vs Swiss German [reference]: OR 0.61, 95% CI 0.46-0.80) were associated with a lower likelihood for app uptake. Further analysis in a randomly selected subsample (n=712) with more detailed information showed that higher levels of trust in government and health authorities were also associated with a higher likelihood for app uptake (ie, high vs low [reference] trust: OR 3.13, 95% CI 1.58-6.22). The most frequent reasons for app nonuse were lack of perceived benefit of using the app (297/808, 36.8%), followed by the lack of a compatible phone (184/808, 22.8%), and privacy concerns (181/808, 22.4%). Conclusions Eliminating technical hurdles and communicating the benefits of digital proximity tracing apps are crucial to promote further uptake and adherence of such apps and, ultimately, enhance their effectiveness to aid pandemic mitigation strategies.

Validly measuring sensitive issues such as norm violations or stigmatizing traits through self-reports in surveys is often problematic. Special techniques for sensitive questions like the Randomized Response Technique (RRT) and, among its variants, the recent crosswise model should generate more honest answers by providing full response privacy. Different types of validation studies have examined whether these techniques actually improve data validity, with varying results. Yet, most of these studies did not consider the possibility of false positives, i.e., that respondents are misclassified as having a sensitive trait even though they actually do not. Assuming that respondents only falsely deny but never falsely admit possessing a sensitive trait, higher prevalence estimates have typically been interpreted as more valid estimates. If false positives occur, however, conclusions drawn under this assumption might be misleading. We present a comparative validation design that is able to detect false positives without the need for an individual-level validation criterion — which is often unavailable. Results show that the most widely used crosswise-model implementation produced false positives to a nonignorable extent. This defect was not revealed by several previous validation studies that did not consider false positives — apparently a blind spot in past sensitive question research.

Background The COVID-19 pandemic challenges societies in unknown ways, and individuals experience a substantial change in their daily lives and activities. Our study aims to describe these changes using population-based self-reported data about social and health behavior in a random sample of the Swiss population during the COVID-19 pandemic. The aim of the present article is two-fold: First, we want to describe the study methodology. Second, we want to report participant characteristics and study findings of the first survey wave to provide some baseline results for our study. Methods Our study design is a longitudinal online panel of a random sample of the Swiss population. We measure outcome indicators covering general well-being, physical and mental health, social support, healthcare use and working state over multiple survey waves. Results From 8,174 contacted individuals, 2,026 individuals participated in the first survey wave which corresponds to a response rate of 24.8%. Most survey participants reported a good to very good general life satisfaction (93.3%). 41.4% of the participants reported a worsened quality of life compared to before the COVID-19 emergency and 9.8% feelings of loneliness. Discussion The COVID-19 Social Monitor is a population-based online survey which informs the public, health authorities, and the scientific community about relevant aspects and potential changes in social and health behavior during the COVID-19 emergency and beyond. Future research will follow up on the described study population focusing on COVID-19 relevant topics such as subgroup differences in the impact of the pandemic on well-being and quality of life or different dynamics of perceived psychological distress.