A0775
Title: Improving measurement error and representativeness in nonprobability surveys
Authors: Aditi Sen - University of Maryland, College Park (United States) [presenting]
Partha Lahiri - University of Maryland College Park (United States)
Abstract: In the age of big data, nonprobability surveys are becoming increasingly abundant. Data integration techniques involving both probability and nonprobability surveys are being extensively used for providing improved estimates for finite population estimation. While much of the existing research has focused on mitigating selection bias in nonprobability surveys, the issue of measurement error within these surveys remains relatively unexplored. Statistical methods devised with the purpose of reducing selection bias are appropriate for reliable estimation, only under the assumption of the accuracy of survey responses. Motivated by a recent case study, the research addresses bias from both measurement and sampling errors in nonprobability surveys. A new data integration method is proposed that leverages machine learning models to construct a composite estimator. The composite estimator integrates probability and nonprobability surveys when both contain response variables of interest. The performance of this estimator is analyzed in comparison to an existing composite estimator in literature, analytically as well as empirically, using multiple survey data from a recent study. Finally, conditions are identified under which the proposed estimator outperforms estimators based solely on probability surveys.
