Machine Learning Techniques for Survey Nonresponse Adjustment

Thomas M. Menino Convention & Exhibition Center 

Nearly all surveys have suffered a secular decline in response rates in the last two decades, raising growing concerns about potential nonresponse bias. In recent years NORC has helped pioneer the use of big data classifiers – machine learning models which use address-level data to target subpopulations of interest at the time of sample design. These address-level big data remain available after sampling for further modelling when survey data collection is complete. We conduct an empirical analysis of the use of these big data sources at the nonresponse adjustment stage of weighting. Machine learning models are used to predict response propensities, and the performance of the resulting nonresponse adjusted weights are compared to weights derived from more traditional methods of nonresponse adjustment.

Machine Learning

Big Data

Survey Non-Response

Survey Statistics 

Survey Research Methods Section