Using Paradata to Predict and Correct for Panel Attrition
Using Paradata to Predict and Correct for Panel Attrition
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DOI：
10.1177/0894439315587258
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发表时间：
2016-06-01
影响因子：
4.1
通讯作者：
Gummer, Tobias
中科院分区：
文献类型：
--
作者：
Rossmann, Joss;Gummer, Tobias
本文讨论了paradigm是否可以帮助我们改进面板损耗模型，以及paradigm是否可以提高倾向得分权重在减少损耗偏差方面的有效性。Paramount的主要优点是它是作为调查过程的副产品收集的。然而，它仍然是一个悬而未决的问题，哪些参数可以用来模拟流失，以及在何种程度上这些参数与感兴趣的变量相关。我们的分析使用了来自七波网络小组调查的数据，该调查由三个横断面调查补充。这种分组设计使我们能够评估大量实质性变量的流失偏倚的程度。此外，这种设计使我们能够详细分析倾向评分权重的有效性。我们的研究结果表明，一些paradox（例如，响应时间和参与历史）提高了小组流失的预测，而其他人没有。此外，并非所有增加模型拟合的参数都能产生有效降低偏倚的权重。这些发现突出了选择与调查答复过程和感兴趣的变量都有联系的参与者的重要性。这篇文章提供了对这一挑战的第一个贡献。
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.