Allocating data collection resources to maximize the analytic utility of a survey data set requires many quick decisions informed by a variety of forecasts and projections. Assumed eligibility and response rates are not always realized and adjustments need to be made during data collection to meet the objectives of the survey. The 2019 National Survey of Early Care and Education (NSECE) is a nationally representative study that characterizes the use and availability of early care and education (ECE) in the United States. The study includes four coordinated nationally-representative surveys of households with young children and providers of ECE. All surveys are multi-mode, and have unknown and highly variable eligibility rates at the start of data collection. This presentation will discuss metrics used to monitor data collection activities in an effort to ensure representativeness of the sample, completeness of surveys, and performance of field interviewers. We will also review adaptive measure taken during the fielding period, such as utilization of replicates, subsampling, and commercial information to enhance efficiencies and representativeness of the sample.