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

Authors: Biemer, Paul P.; Chen, Patrick; Wang, Kevin

Source: Journal of the Royal Statistical Society: Series A (Statistics in Society), Volume 176, Number 1, 1 January 2013 , pp. 147-168(22)

Publisher: Wiley-Blackwell

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Abstract:

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.

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-985X.2012.01058.x

Affiliations: 1: RTI International, Research Triangle Park, USA

Publication date: 2013-01-01