The Blind Spot: Studying the Association Between Survey Nonresponse and Adherence to COVID-19 Governmental Regulations in a Population-Based German Web-Survey

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DOI:

https://doi.org/10.18148/srm/2022.v16i3.7901

Keywords:

COVID-19, nonresponse bias, unit nonresponse, Heckman selection, rule compliance

Abstract

A multitude of COVID-19 studies provide information on adherence to COVID-19 regulations. Although selective non-response might question the validity of generalising study findings, the issue has, as yet, received only little attention. Presumably, the decision as to whether to participate in a COVID-19 study is based on a similar decision-making process as that concerning adherence to COVID-19 regulations. Common characteristics might predict both outcomes which would result in overestimated mean levels and a biased predictor structure of adherence to COVID-19 regulations. We used a random sample of adolescents (born 2001–2003) from the German family panel study pairfam who were first (face-to-face) interviewed in winter 2018/19 and were invited to participate in a (web-based) follow-up COVID-19-interview in spring 2020. Using a simple weighting procedure and Heckman selection models, we found an overestimated mean of adherence to COVID-19 regulations, with the association with gender being overestimated and that with education and migration background underestimated. The extent of bias was less severe and fewer variables were affected than expected. We suggest including a set of additional variables into the models to tackle the bias in the predictor structure and to address mean level bias by using weights accounting for population characteristics. Although COVID-19 studies indeed appear to provide biased results, being able to reduce this bias is generally good news for high-quality COVID-19 studies.

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Published

2022-12-10

How to Cite

Wetzel, M., & Hünteler, B. (2022). The Blind Spot: Studying the Association Between Survey Nonresponse and Adherence to COVID-19 Governmental Regulations in a Population-Based German Web-Survey. Survey Research Methods, 16(3), 267–281. https://doi.org/10.18148/srm/2022.v16i3.7901

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