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?:abstract
  • This article addresses the questions of whether paradata can help us to improve the models of panel attrition and whether paradata can improve the effectiveness of propensity score weights with respect to reducing attrition biases. The main advantage of paradata is that it is collected as a by- product 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 the variables of interest. Our analysis used data from a seven-wave web-based panel survey that had been supplemented by three cross-sectional surveys. This split panel design allowed us to assess the magnitude of attrition bias for a large number of substantive variables. Furthermore, this design enabled us to analyze in detail the effectiveness of propensity score weights. Our results showed that some paradata (e.g., response times and participation history) improved the prediction of panel attrition, whereas others did not. In addition, not all the paradata that increased the model fit resulted in weights that effectively reduced bias. These findings highlight the importance of selecting paradata that are linked to both the survey response process and the variables of interest. This article provides a first contribution to this challenge. (xsd:string)
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?:comment
  • (GLES) (xsd:string)
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  • GLES-Bibliography (xsd:string)
?:dateCreated
  • 6. Fassung, Januar 2017 (xsd:gyear)
?:dateModified
  • 2016 (xsd:gyear)
?:datePublished
  • 2016 (xsd:gyear)
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?:fromPage
  • 312 (xsd:string)
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?:issueNumber
  • 3 (xsd:string)
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  • Using Paradata to Predict and Correct for Panel Attrition (xsd:string)
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  • article (xsd:string)
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?:sourceInfo
  • Bibsonomy (xsd:string)
  • In Social Science Computer Review, 34(3), 312-332, 2016 (xsd:string)
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  • German Longitudinal Election Study (GLES) (xsd:string)
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  • 2016 (xsd:string)
  • FDZ_Wahlen (xsd:string)
  • GLES (xsd:string)
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  • 332 (xsd:string)
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  • 34 (xsd:string)