ABSTRACT

Digital trace data have become one of the central pillars of media research methods. Despite the opportunities for better understanding individual users’ true behaviors in the personalized media environment, many scholars have pointed out the potential for bias in trace data collections, questioning the generalizability of findings based on them. In this study, we propose two statistical bias correction methods–Inverse Probability Weighting (IPW) and Type II Tobit, which are designed to remedy selection bias of inference from digital trace data donated by research participants. Applying these methods to Facebook take-out data, we demonstrate how the correction methods can change estimated effect sizes, which is important for the translation of academic findings into real-world impacts. We conduct two simulation studies, one under fully synthetic and another under partially simulated conditions, and find that Type II Tobit generally provides a more robust and cost-efficient correction method for digital trace data.

摘要

数字追踪数据已成为媒体研究方法的核心支柱之一。尽管有机会更好地了解个性化媒体环境中个人用户的真实行为，但许多学者指出了跟踪数据收集中存在偏见的可能性，并质疑基于它们的发现的普遍性。在这项研究中，我们提出了两种统计偏差校正方法——逆概率加权 (IPW) 和 II 型 Tobit，旨在纠正研究参与者捐赠的数字跟踪数据中的推理选择偏差。将这些方法应用于 Facebook 外卖数据，我们展示了校正方法如何改变估计的效果大小，这对于将学术发现转化为现实世界的影响非常重要。我们进行了两项模拟研究，