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Abstract
The use of mixed-mode surveys has grown rapidly in recent years, due to both technological advances and the COVID-19 pandemic. The increased mixing of modes (and the adoption of newer digital modes like web and text messaging) necessitates an evaluation of the impact of these newer designs on survey errors and costs, as well as new techniques for disaggregating and adjusting for nonresponse and measurement errors. This special issue highlights recent innovations, applications, and evaluations of mixed-mode survey designs and identifies areas where additional research is required.

AbstractMonetary incentives are frequently used to improve survey response rates. While it is common to use a single incentive amount for an entire sample, allowing the incentive to vary inversely with the expected probability of response may help to mitigate nonresponse and/or nonresponse bias. Using data from the 2016 National Household Education Survey (NHES:2016), an address-based sample (ABS) of US households, this article evaluates an experiment in which the noncontingent incentive amount was determined by a household's predicted response propensity (RP). Households with the lowest RP received $10, those with the highest received $2 or $0, and those in between received the standard NHES incentive of $5. Relative to a uniform $5 protocol, this "tailored" incentive protocol slightly reduced the response rate and had no impact on observable nonresponse bias. These results serve as an important caution to researchers considering the targeting of incentives or other interventions based on predicted RP. While preferable in theory to "one-size-fits-all" approaches, such differential designs may not improve recruitment outcomes without a dramatic increase in the resources devoted to low RP cases. If budget and/or ethical concerns limit the resources that can be devoted to such cases, RP-based targeting could have little practical benefit.