The core principle behind RIWI’s technology is that we are able to reach as diverse an audience as possible online, and that any Web user has a chance of randomly coming upon a RIWI survey or message test. RIWI’s Random Domain Intercept Technology (RDIT) introduces opt-in Web surveys and message tests to people surfing online who click on links or type in websites that are dormant. This means that instead of encountering a “page does not exist” notification or ad, a RIWI survey or message test is rendered full-site on the page. Survey participants are accessed on all Web-enabled devices, including desktop computers, tablets, smartphones, etc. Upon encountering the survey or message test, individuals decide whether they would like to anonymously participate in the research. No incentives are provided, and respondents may exit the survey at any time.
RDIT is safe and anonymous:
- IP addresses (unique identifiers) are not ingested in order to protect participant privacy and to enhance security.
- RIWI collects and reports the geo-location and self-reported age and gender of all respondents; however, no personally identifiable information is stored or reported, ensuring respondent privacy and safety.
- No response is traceable to an individual; re-identification of respondents using machine learning tools is not possible since respondents do not provide, nor does RIWI collect, personally identifiable information.
- RIWI respondents are advised of their anonymity, security, and privacy when they randomly access a RIWI survey.
RDIT mitigates participant bias and results in diverse samples:
- The audience exposed in any world region is geographically representative of the online population of that region. RIWI groups respondents automatically by region, city, and sub-city area, and provides participants with a language- and context-appropriate survey or message test.
- No incentives for responding are offered — which eliminates the possibility of incentive bias — and participants are able to exit the survey at any time. Because respondents are not given incentives to participate, they answer because the topic holds salience for them or because surveys or message tests are somewhat of a novelty.
- Since the survey or message test sites do not have ad tracking pixels, ad block technology does not materially reduce the size or diversity of the sample.
- RDIT’s systems, as detailed above, reduce self-selection bias, social desirability bias, acquiescence bias, and online coverage bias.
- Non-respondent bias for any distinct survey or message test is unknown since the sampling frame is random and anonymous in order to maximize the diversity of potential respondents. However, the representativeness of unweighted self-reported data, such as education levels, can be compared to census to ensure the generalizability of the results.
RDIT results in high-quality data:
- RIWI uses continuous bot-filtering and anomaly detection (e.g., straight-lining detection) to ensure answers are authentic human responses.
Points of entry to the survey or message test (i.e., lapsed Web domains) rotate regularly, ensuring that respondents are unique and have not previously completed the survey or message test.
- RIWI specializes in survey instrument and message test optimization to reduce respondent fatigue on all device screens and bandwidths. This includes the development and utilization of clear and concise language for ease of comprehension.
- For data integrity and transparency, data are provided in real-time in unweighted format, and also weighted to the population of interest. Weights are applied to age and gender according to the most recent census data available for the region. Respondent weight values are generated post-stratification using a raking algorithm. Statistical significance tests (Chi-square) are applied for all variable cross-tabulations in real-time.
RDIT is peer reviewed:
- Studies using RDIT have been published in top journals across a variety of disciplines. Select representative publications are linked below.
Select Representative Publications
Sargent RH, Laurie S, Weakland LF, Lavery JV, Salmon DA, Orenstein WA, Breiman RF (2022). Use of Random Domain Intercept Technology to Track COVID-19 Vaccination Rates in Real Time Across the United States: Survey Study. J Med Internet Res 2022;24(7):e37920. https://www.jmir.org/2022/7/e37920/
Roder-DeWan, S., Gage, A. D., Hirschhorn, L. R., Twum-Danso, N. A., Liljestrand, J., Asante-Shongwe, K., Rodríguez, V., Yahya, T., & Kruk, M. E. (2019). Expectations of healthcare quality: A cross-sectional study of internet users in 12 low-and middle-income countries. PLoS Medicine, 16(8), e1002879. https://doi.org/10.1371/journal.pmed.1002879
Roder-DeWan, S., Gage, A., Hirschhorn, L. R., Twum-Danso, N. A., Liljestrand, J., Asante-Shongwe, K., Yahya, T., & Kruk, M. (2020). Level of confidence in and endorsement of the health system among internet users in 12 low-income and middle-income countries. BMJ Global Health, 5(8), e002205. https://dx.doi.org/10.1136/bmjgh-2019-002205
Sargent, R. H., Laurie, S., Moncada, L., Weakland, L. F., Lavery, J. V., Salmon, D. A., Orenstein, W. A., & Breiman, R. F. (2022). Masks, money, and mandates: A national survey on efforts to increase COVID-19 vaccination intentions in the United States. PloS One, 17(4), e0267154. https://doi.org/10.1371/journal.pone.0267154
Seeman, N. L., & Seeman, M. V. (2020). An anonymous survey method for obtaining sensitive, personal, or embarrassing information in the context of mental illness stigma. In SAGE Research Methods Cases: Medicine and Health. SAGE Publications, Ltd. https://dx.doi.org/10.4135/9781529719260