Reducing Respondent Burden with Efficient Survey Invitation Design

Increasing costs of data collection and decreasing response rates in social surveys has led to a proliferation of mixed-mode and self-administered surveys. In this context the design and content of survey invitations is increasingly important as it influences propensities to participate. By reducing...

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Bibliographic Details
Main Authors: Hafsteinn Einarsson, Alexandru Cernat, Natalie Shlomo
Format: Article in Journal/Newspaper
Language:English
Published: European Survey Research Association 2021
Subjects:
Online Access:https://doi.org/10.18148/srm/2021.v15i3.7777
https://doaj.org/article/23dd21277f45467e85e94816ba763e8a
Description
Summary:Increasing costs of data collection and decreasing response rates in social surveys has led to a proliferation of mixed-mode and self-administered surveys. In this context the design and content of survey invitations is increasingly important as it influences propensities to participate. By reducing the respondents’ burden of engaging with the survey invitation survey organisations can streamline the participation process. Reducing respondent burden by efficient invitation design may increase the number of early responders, the number overall responses and reduce non-response bias. This study implemented a randomised experiment where two design features thought to be associated with respondent burden were randomly manipulated: the length of the text and the location of the survey invitation link. The experiment was carried out in a sequential mixed-mode survey among young adults (18-35-year-old) in Iceland, where design features (text length and survey link location) of mailed letters with links to a web survey were manipulated. Results show that participants are more likely to participate in the survey when they receive shorter survey invitation texts and with survey links in the middle Additionally, short letters with links in the middle perform well compared to other letter types in terms of non-response bias and mean squared error.