likely to complete our Facebook survey if they had personally experienced challenges related to the survey topics. This may often be the case for internet surveys that explicitly focus on problems or challenging circumstances experienced by the respondents. However, not all internet surveys estimate more severe outcomes than their more traditional counterparts. For example, a recent working paper by Soundararajan et al (2023) finds that across six countries Random Domain Intercept Technology (RDIT) surveys tend to overestimate employment-to-population ratios.

In addition, the extent of potential selection and misreporting bias is likely to be affected by survey framing as well as topic and context. Some features of the Facebook survey framing in our example may have exacerbated selection and misreporting bias, and careful design of future internet surveys could reduce that contribution.

# **Taking advantage of opportunities for comparison to establish a baseline**

Internet surveys may be useful for rapid assessment, with the understanding that they will present an incomplete picture relative to the overall population. The MENA Data Lab, for example, has supported a handful of surveys on food insecurity and price expectations across multiple countries in North Africa amid recent concerns over inflation. In contexts in which volatility is expected, it could be valuable to take advantage of opportunities to pair a more traditional survey with a simultaneous online survey covering the same content. This is relatively simple and inexpensive to do, and it establishes a baseline for future online surveys in the event of a shock or otherwise rapidly changing circumstances.

# **Maximizing the potential of internet surveys to triangulate responses to sensitive questions**

Internet surveys may be able to add value to their more traditional counterparts by offering a different perspective on sensitive questions based on more honest reporting, but that value is reduced if we cannot figure out how much of the difference between traditional and internet estimates is due to more honest reporting and how much is due to other factors.

To help isolate differences due to more honest reporting and minimize other sources of difference, future comparison efforts might consider:

- Assessing at the design stage which other mechanisms might operate alongside sensitivity bias to produce differences between the planned surveys, what can be predicted about their relative size and direction, and the extent to which they could be minimized in the survey design

- Including a sufficient set of non-sensitive (or at least substantially less sensitive) questions to be able to compare between-survey differences in sensitive and non-sensitive questions

- Framing introductory materials in general terms that avoid presenting potentially sensitive topics as the focus of the survey

# **Future questions to explore**

Further research into the potential value of internet surveys for sensitive topics could compare the results of an online questionnaire advertised on Facebook or other social media platform with one collected using RDIT to assess whether accessing the survey via a social media platform reduces respondent perceptions of anonymity. The online survey modality also offers low-cost opportunities to investigate interactions between questionnaire design and sensitive response behavior, for example by randomizing the order in which modules are presented to test for potential framing effects.

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