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Bradley et al. (arXiv:2106.05818v2), as part of an analysis of theperformance of large-but-biased surveys during the COVID-19 pandemic, arguethat the data defect correlation provides a useful tool to quantify the effectsof sampling bias on survey results. We examine their analyses of results fromthe COVID-19 Trends and Impact Survey (CTIS) and show that, despite theirclaims, CTIS in fact performs well for its intended goals. Our examinationreveals several limitations in the data defect correlation framework, includingthat it is only applicable for a single goal (population point estimation) andthat it does not admit the possibility of measurement error. Through examples,we show that these limitations seriously affect the applicability of theframework for analyzing CTIS results. Through our own alternative analyses, wearrive at different conclusions, and we argue for a more expansive view ofsurvey quality that accounts for the intended uses of the data and all sourcesof error, in line with the Total Survey Error framework that have been widelystudied and implemented by survey methodologists.

Big data, big problems: Responding to "Are we there yet?"