Data Fusion for Correcting Measurement Errors

TL;DR

This paper introduces a data fusion framework that leverages high-quality auxiliary data to correct measurement errors in survey data, improving inference accuracy without relying on the typical independence assumptions.

Contribution

It develops a novel modeling approach for measurement error correction using data fusion, accounting for error rates and reporting behaviors, and applies it to real survey data.

Findings

Improved accuracy in estimating educational attainment distributions.

Effective correction of measurement errors using auxiliary high-quality data.

Sensitivity analysis methods for measurement error models.

Abstract

Often in surveys, key items are subject to measurement errors. Given just the data, it can be difficult to determine the distribution of this error process, and hence to obtain accurate inferences that involve the error-prone variables. In some settings, however, analysts have access to a data source on different individuals with high quality measurements of the error-prone survey items. We present a data fusion framework for leveraging this information to improve inferences in the error-prone survey. The basic idea is to posit models about the rates at which individuals make errors, coupled with models for the values reported when errors are made. This can avoid the unrealistic assumption of conditional independence typically used in data fusion. We apply the approach on the reported values of educational attainments in the American Community Survey, using the National Survey of College Graduates as the high quality data source. In doing so, we account for the informative sampling design used to select the National Survey of College Graduates. We also present a process for assessing the sensitivity of various analyses to different choices for the measurement error models. Supplemental material is available online.