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?:abstract
  • Attrition is an important methodological challenge to panel surveys (Lynn 2009). Still, there is a remarkable shortage of variables which are associated with both, the propensity of respondents to stay in the panel and the variables of interest. As a result, propensity score weights which are designed to correct for this type of nonresponse frequently yield mixed results. This paper addresses the question whether paradata can successfully be applied to improve the prediction of attrition in panel Web surveys. Their main advantage is that they are collected as a byproduct of the survey process. However, it is still an open question which paradata can be used to model attrition and to what extent these paradata are correlated with variables of interest (Kreuter and Olson 2013). We use logistic regressions to model attrition in a 7-wave panel Web survey (n=5,256) and to compute propensity score weights. The weights are based on different models fitted with sets of socio-demographic, substantial, survey evaluation, and paradata variables. The latter include measures of response times, user agent strings to determine the device used by the respondent, as well as indicators of the respondents’ response behavior. Finally, we use supplemental cross-sectional Web surveys to assess the effectiveness of propensity score weights based on different sets of variables. This paper enhances the existing knowledge in several ways: It presents a set of paradata variables and provides empirical tests of their capability to correct for panel attrition. We show that these paradata can successfully be used to create auxiliary data in a cost-efficient way. At the same time, we demonstrate that they do not ultimately help to correct for panel attrition. Thus, we conclude that further research on the link between paradata, panel attrition and correction methods is needed. (xsd:string)
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  • (GLES) (xsd:string)
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  • GLES-Bibliography (xsd:string)
?:dateCreated
  • 4. Fassung, Februar 2015 (xsd:gyear)
?:dateModified
  • 2014 (xsd:gyear)
?:datePublished
  • 2014 (xsd:gyear)
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  • Does the use of paradata in propensity score weighting improve the correction of panel attrition in a web-based survey? (xsd:string)
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  • inproceedings (xsd:string)
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  • 6. European Congress of Methodology (xsd:string)
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  • Bibsonomy (xsd:string)
  • In 6. European Congress of Methodology, 2014 (xsd:string)
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  • 23.07.-25.07.2014 (xsd:gyear)
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  • German Longitudinal Election Study (GLES) (xsd:string)
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  • 2014 (xsd:string)
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  • GLES (xsd:string)
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  • GLES_version4 (xsd:string)
  • ZA5705 (xsd:string)
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  • inproceedings (xsd:string)
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