The use of individual-level browsing data, that is, the records of a person’s visits to online content through a desktop or mobile browser, is of increasing importance for social scientists. Browsing data have characteristics that raise many questions for statistical analysis, yet to date, little hands-on guidance on how to handle them exists. Reviewing extant research, and exploring data sets collected by our four research teams spanning seven countries and several years, with over 14,000 participants and 360 million web visits, we derive recommendations along four steps: preprocessing the raw data; filtering out observations; classifying web visits; and modelling browsing behavior. The recommendations we formulate aim to foster best practices in the field, which so far has paid little attention to justifying the many decisions researchers need to take when analyzing web browsing data.

中文翻译：

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Web 浏览数据分析：指南

个人层面的浏览数据（即一个人通过桌面或移动浏览器访问在线内容的记录）的使用对于社会科学家来说越来越重要。浏览数据的特征会给统计分析带来许多问题，但迄今为止，几乎没有关于如何处理这些数据的实际指导。回顾现有研究，并探索我们的四个研究团队跨越七个国家、历时数年、超过 14,000 名参与者和 3.6 亿次网络访问量收集的数据集，我们按照四个步骤得出建议：预处理原始数据；过滤掉观察结果；对网络访问进行分类；以及对浏览行为进行建模。我们制定的建议旨在促进该领域的最佳实践，迄今为止，该领域很少关注证明研究人员在分析网络浏览数据时需要做出的许多决策的合理性。