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Obtaining Record Linkage Consent: Results from a Wording Experiment in Germany

[journal article]

Sakshaug, Joseph W.; Wolter, Stefanie; Kreuter, Frauke

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Please use the following Persistent Identifier (PID) to cite this document:http://nbn-resolving.de/urn:nbn:de:0168-ssoar-462454

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Abstract Many sample surveys ask respondents for consent to link their survey information with administrative sources. There is significant variation in how linkage requests are administered and little experimental evidence to suggest which approaches are useful for achieving high consent rates. A common approach is to emphasize the positive benefits of linkage to respondents. However, some evidence suggests that emphasizing the negative consequences of not consenting to linkage is a more effective strategy. To further examine this issue, we conducted a gain-loss framing experiment in which we emphasized the benefit (gain) of linking or the negative consequence (loss) of not linking one’s data as it related to the usefulness of their survey responses. In addition, we explored a sunk-prospective costs rationale by varying the emphasis on response usefulness for responses that the respondent had already provided prior to the linkage request (sunk costs) and responses that would be provided after the linkage request (prospective costs). We found a significant interaction between gain-loss framing and the sunk-prospective costs rationale: respondents in the gain-framing condition consented to linkage at a higher rate than those in the loss-framing condition when response usefulness was emphasized for responses to subsequent survey items. Conversely, the opposite pattern was observed when response usefulness was emphasized for responses that had already been provided: loss-framing resulted in a higher consent rate than the gain-framing, but this result did not reach statistical significance.
Keywords Federal Republic of Germany; survey research; survey; response behavior; data organization; personal data; data capture; official statistics; social data; experiment
Classification Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Document language English
Publication Year 2015
Page/Pages 12 p.
Journal Survey Methods: Insights from the Field (2015)
DOI http://dx.doi.org/10.13094/SMIF-2015-00012
ISSN 2296-4754
Status Published Version; peer reviewed
Licence Creative Commons - Attribution-Noncommercial-No Derivative Works