Digital trace data are receiving increased attention as a potential way to capture human behavior. Nevertheless, this type of data is far from perfect and may not always provide better data compared to traditional social surveys. In this study we estimate measurement quality of survey and digital trace data on smartphone usage with a MultiTrait MultiMethod (MTMM) model. The experimental design included five topics relating to the use of smartphones (traits) measured with five methods: three different survey scales (a 5- and a 7-point frequency scale and an open-ended question on duration) and two measures from digital trace data (frequency and duration). We show that surveys and digital trace data measures have very low correlation with each other. We also show that all measures are far from perfect and, while digital trace data appears to have often better quality compared to surveys, that is not always the case.

中文翻译：

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使用 MultiTrait MultiMethod 模型估计数字追踪数据和调查的测量质量


数字跟踪数据作为捕获人类行为的潜在方式受到越来越多的关注。然而，此类数据远非完美，与传统社会调查相比，可能并不总是提供更好的数据。在本研究中，我们使用 MultiTrait MultiMethod (MTMM) 模型评估智能手机使用情况的调查和数字跟踪数据的测量质量。实验设计包括与智能手机使用（特征）相关的五个主题，通过五种方法进行测量：三种不同的调查量表（5 点和 7 点频率量表以及关于持续时间的开放式问题）以及来自数字轨迹的两种测量数据（频率和持续时间）。我们表明，调查和数字追踪数据测量之间的相关性非常低。我们还表明，所有措施都远非完美，虽然数字追踪数据似乎通常比调查具有更好的质量，但情况并非总是如此。