Abstract：
Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection.
摘要：
移动应用程序为心理数据收集提供了广泛的机会，例如与传统的实验室研究相比，增加了生态有效性和参与者的接受度。然而,基于应用程序的心理数据也带来了数据分析的挑战，因为错误和观察的相互依赖带来了复杂性。因此,研究人员必须权衡基于应用程序的数据收集的利弊，以决定他们提出的应用程序研究的科学效用。例如,一些研究只有在提供足够的统计能力的情况下才有价值。然而,app数据的复杂性阻碍了使用简单的分析公式来估计功率等属性。在本文中,我们演示了如何使用蒙特卡洛模拟来研究应用程序使用行为对基于应用程序的心理数据的效用的影响。我们介绍了一组问题来指导模拟实施，并在猜谜游戏应用程序WhoKnows（Rau等人。,2023年）。最后,我们简要概述了仿真结果以及从它们中得出的用于实际数据生成的结论。我们的结果可以作为如何使用模拟方法来计划基于现实世界的基于应用程序的数据收集的示例。
