and disadvantages to each of these approaches,<sup>5</sup> there are some distinct advantages to using an existing F2F survey when it comes to counteracting coverage and nonresponse bias. The F2F survey that the phone sample is drawn from will typically contain detailed information across a wide variety of domains not just for interviewed households but also households that were ineligible (i.e. did not have access to a mobile phone) and those that were not successfully interviewed (i.e. those that did not respond or refused). This wealth of information can be harnessed to more effectively reduce bias in the phone survey sample. RDD surveys and surveys utilizing network provider frames often do not have the luxury of having detailed information on the ineligible and nonresponding sample to attempt to reduce the bias and thus any bias adjustments employed for those surveys are potentially inferior to those from representative F2F surveys.<sup>6</sup> The most prominent method to achieve a reduction in bias in telephone surveys is through adjustments to the survey weights. In order to implement these weighting adjustments, additional information is required to understand and attempt to correct for the bias. External information on known demographic composition of the general population (e.g. from population census) can be compared with that of the interviewed sample and the weights adjusted to counteract that balance. Such external information is usually limited to basic demographics. More detailed information from recent representative F2F surveys (e.g. LSMS, Demographic and Health Surveys, Household Budget Surveys, etc.) could similarly be compared with the interviewed sample. However, for RDD and telecom list-based phone surveys, information on these characteristics must also captured in the phone survey which is not always feasible in a phone survey format where the scope (in terms of question complexity) and length (in terms of interview time) are much reduced compared to a typical F2F surveys. For example, it is not feasible to collect all information necessary to construct an aggregate measure of household consumption expenditures, the prevailing welfare measure in many developing countries. Phone surveys utilizing existing representative surveys as a frame have much more scope for effective bias adjustments through reweighting as a result of the rich set of information available for the representative sample that serves as a frame. There are a variety of approaches to counteract the biases present in phone surveys utilizing the detailed information from the representative F2F survey, though three prominent methods are explored here.

The first is through **weighting class adjustments,** whereby the phone survey sample is divided into cells that represent different “classes” of respondents (Little, 1986). These cells are typically constructed by crossing several characteristics that may be correlated with the likelihood of nonresponse (e.g. gender, age, education, location, etc.). The phone survey response rate is then calculated for each of these cells and the weights for interviewed households in the cell increased by the inverse of the response rate.

> 5 See Himelein et al (2020) for a more complete comparison of the advantages and disadvantages of these three sampling approaches.

6 One disadvantage of using a representative F2F survey as a frame (compared to RDD and list-based frames) is that F2F surveys are often a two-stage clustered design with census enumeration areas typically serving as the clusters from which households/respondents are selected. This clustered design introduces design effects that reduces the effective sample size and increases the size of the overall sample required to achieve the desired level of precision (relative to a simple random sample). These design effects are at least partially retained in a phone sample drawn from the clustered F2F survey sample. These imported design effects imply that a larger sample is required to achieve the same level of precision for a phone survey drawn from a clustered F2F survey than an RDD or list-based phone survey (which is not clustered).

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