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A data-driven approach to monitoring data collection in an online panel
Longitudinal and Life Course Studies ( IF 1.122 ) Pub Date : 2019-10-01 , DOI: 10.1332/175795919x15694136006114
Jessica M.E. Herzing , Caroline Vandenplas , Julian B. Axenfeld

Longitudinal or panel surveys suffer from panel attrition which may result in biased estimates. Online panels are no exceptions to this phenomenon, but offer great possibilities in monitoring and managing the data collection phase and response-enhancement features (e.g., reminders), due to real-time availability of paradata. This paper presents a data-driven approach to monitor the data collection phase and to inform the adjustment of response-enhancement features during data collection across online panel waves, which takes into account the characteristics of an ongoing panel wave. For this purpose, we study the evolution of the daily response proportion in each wave of a probability-based online panel. Using multilevel models, we predict the data collection evolution per wave day. In our example, the functional form of the data collection evolution is quintic. The characteristics affecting the shape of the data collection evolution are characteristics of the specific wave day and not of the panel wave itself. In addition, we simulate the monitoring of the daily response proportion of one panel wave and find that the timing of sending reminders could be adjusted after 20 consecutive panel waves to keep the data collection phase efficient. Our results demonstrate the importance of re-evaluating the characteristics of the data collection phase, such as the timing of reminders, across the lifetime of an online panel to keep the fieldwork efficient.

中文翻译:

一种数据驱动的方法来监视在线面板中的数据收集

纵向调查或小组调查会遭受小组减员,这可能导致估计偏差。在线面板也不是这种现象的例外,但是由于paradata的实时可用性,在线面板在监视和管理数据收集阶段以及响应增强功能(例如提醒)方面提供了极大的可能性。本文提出了一种数据驱动的方法来监视数据收集阶段并通知在线面板波数据收集过程中响应增强功能的调整,其中考虑了正在进行的面板波的特征。为此,我们研究了基于概率的在线小组的每一波中每日响应比例的演变。使用多层模型,我们可以预测每个波浪日的数据收集演变。在我们的示例中 数据收集演化的功能形式是五次。影响数据收集演化形状的特征是特定波浪日的特征,而不是面板波浪本身的特征。此外,我们模拟了一个面板波的每日响应比例的监视,发现连续20个面板波后可以调整发送提醒的时间,以保持数据收集阶段的效率。我们的结果表明,在整个在线小组的整个生命周期中,重新评估数据收集阶段的特征(如提醒时间)的重要性对于保持现场工作高效有重要意义。我们模拟了对一个面板波的每日响应比例的监视,发现可以在连续20个面板波之后调整发送提醒的时间,以保持数据收集阶段的效率。我们的结果表明,在整个在线小组的整个生命周期中,重新评估数据收集阶段的特征(如提醒时间)的重要性对于保持现场工作高效有重要意义。我们模拟了对一个面板波的每日响应比例的监视,发现可以在连续20个面板波之后调整发送提醒的时间,以保持数据收集阶段的效率。我们的结果表明,在整个在线小组的整个生命周期中,重新评估数据收集阶段的特征(如提醒时间)的重要性对于保持现场工作高效有重要意义。
更新日期:2019-10-01
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