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Now, later, or never? Using response-time patterns to predict panel attrition
[journal article]
Abstract Preventing panel members from attriting is a fundamental challenge for panel surveys. Research has shown that response behavior in earlier waves (response or nonresponse) is a good predictor of panelists' response behavior in upcoming waves. However, response behavior can be described in greater det... view more
Preventing panel members from attriting is a fundamental challenge for panel surveys. Research has shown that response behavior in earlier waves (response or nonresponse) is a good predictor of panelists' response behavior in upcoming waves. However, response behavior can be described in greater detail by considering the time until the response is returned. In the present study, we investigated whether respondents who habitually return their survey late and respondents who switch between early and late response in multiple waves are more likely to attrit from a panel. Using data from the GESIS Panel, we found that later response is related to a higher likelihood of attrition (AME = 0.087) and that response-time stability is related to a lower likelihood of attrition (AME = −0.013). Our models predicted most cases of attrition; thus, survey practitioners could potentially predict future attriters by applying these models to their own data.... view less
Keywords
prognosis; survey; panel; data quality; response behavior; survey research; data capture
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
Reluctant response; panel survey; paradata; late response; ZA5664: GESIS Panel - Extended Edition (Datenfile Version 26.0.0)
Document language
English
Publication Year
2022
Page/Pages
p. 1-14
Journal
International Journal of Social Research Methodology (2022)
DOI
https://doi.org/10.1080/13645579.2022.2091259
ISSN
1464-5300
Status
Published Version; peer reviewed
Licence
Creative Commons - Attribution-Noncommercial-No Derivative Works 4.0
FundingGefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491156185 / Funded by the German Research Foundation (DFG) - Project number 491156185