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Augmenting survey data with digital trace data : Is there a threat to panel retention?
Trappmann, Mark; Schwarz, Stefan; Haas, Georg-Christoph; u. a. (2023): Augmenting survey data with digital trace data : Is there a threat to panel retention?, in: Bamberg: Otto-Friedrich-Universität, S. 541–552.
Faculty/Chair:
Publisher Information:
Year of publication:
2023
Pages:
Source/Other editions:
Journal of survey statistics and methodology : JSSAM, 11 (2022), 3, S. 541–552 . - ISSN: 2325-0992
Year of first publication:
2022
Language:
English
Abstract:
Linking digital trace data to existing panel survey data may increase the overall analysis potential of the data. However, producing linked products often requires additional engagement from survey participants through consent or participation in additional tasks. Panel operators may worry that such additional requests may backfire and lead to lower panel retention, reducing the analysis potential of the data. To examine these concerns, we conducted an experiment in the German PASS panel survey after wave 11. Three quarters of panelists (n = 4,293) were invited to install a research app and to provide sensor data over a period of 6 months, while one quarter (n = 1,428) did not receive an invitation. We find that the request to install a smartphone app and share data significantly decreases panel retention in the wave immediately following the invitation by 3.3 percentage points. However, this effect wears off and is no longer significant in the second and third waves after the invitation. We conclude that researchers who run panel surveys have to take moderate negative effects on retention into account but that the potential gain likely outweighs these moderate losses.
GND Keywords: ;
Panelanalyse
Datenintegration
Keywords: ; ; ; ;
Attrition
Digital trace data
Panel survey
Privacy
Respondent burden
DDC Classification:
RVK Classification:
Peer Reviewed:
Yes:
International Distribution:
Yes:
Type:
Article
Activation date:
July 4, 2023
Permalink
https://fis.uni-bamberg.de/handle/uniba/59900