Do coefficients of variation of response propensities approximate non-response biases during survey data collection?

We evaluate the utility of coefficients of variation of response propensities (CVs) as measures of risks of survey variable non-response biases when monitoring survey data collection. CVs quantify variation in sample response propensities estimated given a set of auxiliary attribute covariates observed for all subjects. If auxiliary covariates and survey variables are correlated, low levels of propensity variation imply low bias risk. CVs can also be decomposed to measure associations between auxiliary covariates and propensity variation, informing collection method modifications and post-collection adjustments to improve dataset quality. Practitioners are interested in such approaches to managing bias risks, but risk indicator performance has received little attention. We describe relationships between CVs and expected biases and how they inform quality improvements during and post-data collection, expanding on previous work. Next, given auxiliary information from the concurrent 2011 UK census and details of interview attempts, we use CVs to quantify the representativeness of the UK Labour Force Survey dataset during data collection. Following this, we use survey data to evaluate inference based on CVs concerning survey variables with analogues measuring the same quantities among the auxiliary covariate set. Given our findings, we then offer advice on using CVs to monitor survey data collection.

Automated summary

This study evaluates the utility of CVs as a risk indicator for survey data collection, quantifying variation in response propensities among samples. CVs can measure the correlation between auxiliary attributes and response propensities, guiding modifications to collection methods and improving dataset quality. Using CVs, the representativeness of survey datasets can be quantified and guidance provided for monitoring survey data collection.