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Using level‐of‐effort paradata in non‐response adjustments with application to field surveys

Summary.  The paper considers the use of level‐of‐effort (LOE) paradata to model the non‐response mechanism in surveys and to adjust for non‐response bias, particularly bias that is missing not at random or non‐ignorable. Our approach is based on an unconditional maximum likelihood estimation (call‐back) model that adapts and extends the prior work to handle the complexities that are encountered for large‐scale field surveys. A test of the ‘missingness at random’ assumption is also proposed that can be applied to essentially any survey when LOE data are available. The non‐response adjustment and the test for missingness at random are then applied and evaluated for a large‐scale field survey—the US National Survey on Drug Use and Health. Although evidence on non‐ignorable non‐response bias was found for this survey, the call‐back model could not remove it. One likely explanation of this result is error in the LOE data. This possibility is explored and supported by a field investigation and simulation study informed by data obtained on LOE errors.