The BLS organization and its staff must devote a greater share of resources to an active and responsive research program that focuses on emerging technologies and behavioral patterns, rather than today’s dominant survey modes. It is important to develop the capacity to study the effects of alternative methods within the context of the actual production survey, not only to evaluate which method is most effective, but also to be able to quantify the impact of a methodological change on key estimates from the survey.
For any project, the focus will be to contribute to the next generation of survey methods as they apply to BLS survey settings, rather than to pursue incremental changes from past methodologies. It is of paramount importance to conceptualize and evaluate methodologies in the context of total survey error and to quantify the impact of multiple sources of error (e.g., coverage, nonresponse, measurement, and processing errors).
To accomplish the above, BLS will have to hire new staff or train existing staff in a variety of areas. Several staff members will need to focus on identifying and acquiring administrative or commercial data to supplement data from the CE surveys. In the process, an evaluation of the quality and accuracy of the administrative records will need to be performed. Given that the CE redesign will involve both pilot and large-scale field tests, staff trained in experimental design will be needed to conduct and evaluate the results of this research. Both quantitative and qualitative methods must be used. The required quantitative skills are relatively well known, but the qualitative methods will go beyond conducting focus groups and cognitive testing. They include the ability to understand the effects of changes in methodology on survey operations and data collection personnel, such as how implementation of new procedures might differ across regional offices.
Given the complexity of BLS surveys and the way they will need to be altered to accommodate the current survey landscape, standard design-based estimators will not suffice. BLS statistical staff will need to develop more current methodological expertise for a range of modern statistical methods. For example, working knowledge is needed of imputation methods