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Now, later, or never? Using response-time patterns to predict panel attrition
[Zeitschriftenartikel]
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... mehr
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.... weniger
Thesaurusschlagwörter
Antwortverhalten; Datengewinnung; Prognose; Befragung; Datenqualität; Panel; Umfrageforschung
Klassifikation
Erhebungstechniken und Analysetechniken der Sozialwissenschaften
Freie Schlagwörter
Reluctant response; panel survey; paradata; late response; ZA5664: GESIS Panel - Extended Edition (Datenfile Version 26.0.0)
Sprache Dokument
Englisch
Publikationsjahr
2022
Seitenangabe
S. 1-14
Zeitschriftentitel
International Journal of Social Research Methodology (2022)
DOI
https://doi.org/10.1080/13645579.2022.2091259
ISSN
1464-5300
Status
Veröffentlichungsversion; begutachtet (peer reviewed)
Lizenz
Creative Commons - Namensnennung, Nicht kommerz., Keine Bearbeitung 4.0
FörderungGefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491156185 / Funded by the German Research Foundation (DFG) - Project number 491156185