Improving Response Quality with Planned Missing Data: An Application to a Survey of Banks

We survey banks to construct national estimates of total noncash payments by type, payments fraud and related information. The survey is designed to create aggregate total estimates of all payments in the United States using data from responses returned by a representative, random sample. In 2016, the number of questions in the survey doubled compared with the previous survey, raising serious concerns of smaller bank nonparticipation. To obtain sufficient response data for all questions from smaller banks, we administered a modified survey design which, in addition to randomly sampling banks, also randomly assigned one of several survey forms, subsets of the full survey. This case study illustrates that while several other factors influenced response outcomes, the approach helped ensure sufficient response for smaller banks. Using such an approach may be especially important in an optional-participation survey, when reducing costs to respondents may affect success, or when imputation of unplanned missing items is already needed for estimation. While a variety of factors affected the outcome, we find that the planned missing data approach improved response outcomes for smaller banks. The planned missing item design should be considered as a way of reducing survey burden or increasing unit-level and item-level responses for individual respondents without reducing the full set of survey items collected.