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Assessing Survey Quality Through Streamlined Data Processing
Donsig Jang
Mathematica Policy Research, Inc
Alicia Haelen
Mathematica Policy Research
Flora Lan
National Center for Science and Engineering Statistics
Amy Beyler
United Healthcare
The technological advancements that make adaptive survey design possible also make it possible to streamline data processing. Survey-management systems can now link data sources in real time, allowing statisticians to conduct editing, imputation, and weighting during data collection. Researchers can even monitor key survey variables during data collection. Combining adaptive survey design with this streamlined process not only allows us to assess data quality and bias during data collection, but it also expedites data processing because it enables us to put all data-processing systems in place by the end of the collection period.
In conducting the National Survey of Recent College Graduates for NSF, we replaced the customary sequential approach to data processing with this integrated approach. This allowed us to test our data-processing procedures, including key SAS programs for autocoding, computer edits, and imputation. We produced and examined real-time quality measures, bias indicators, and paradata, and then assembled a comprehensive quality profile and assessed nonresponse bias. Monitoring the data enabled us to correct problems as they arose.