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ExperienceSampler: An Open-Source Scaffold for Building Smartphone Apps for Experience Sampling

机译:体验采样器:用于构建智能手机应用程序的开源支架

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Experience sampling methods allow researchers to examine phenomena in daily life and provide various advantages that complement traditional laboratory methods. However, existing experience sampling methods may be costly, require constant Internet connectivity, may not be designed specifically for experience sampling studies, or require a custom solution from a computer programming consultant. In this article, we present ExperienceSampler. an open-source scaffold for creating experience-sampling smartphone apps designed for Android and iOS devices. We designed ExperienceSampler to address the common barriers to using experience sampling methods. First, there is no cost to the user. Second, ExperienceSampler-apps make use of local notifications to let participants know when to complete surveys and store the data locally until Internet connection is available. Third, our app scaffold was designed with experience sampling methodological issues in mind. We also demonstrate how researchers can easily customize ExperienceSampler even if they have no programming skills. Furthermore, we evaluate the utility of ExperienceSampler apps with results from one social psychological study conducted using ExperienceSampler (N = 168). Mean response rates averaged 84%, and the median response latency was 9 minutes. Taken together, ExperienceSampler creates cost-effective smartphone apps that can be easily customized by researchers to examine experiences in daily life.
机译:经验抽样方法使研究人员可以检查日常生活中的现象,并提供补充传统实验室方法的各种优势。但是,现有的经验抽样方法可能是昂贵的,需要持续的互联网连接,可能不是专门设计用于体验抽样研究的,或者需要计算机编程顾问的自定义解决方案。在本文中,我们介绍了体验采样器。一个开源脚手架,用于创建专为Android和iOS设备设计的体验采样智能手机应用程序。我们设计了体验采样器,以解决使用体验抽样方法的常见障碍。首先,用户没有成本。其次,体验摄像机应用程序利用本地通知,让参与者知道何时完成调查并在本地存储数据,直到可以使用Internet连接为止。第三,我们的应用程序脚手架的设计考虑了经验抽样的方法论问题。我们还展示了研究人员即使没有编程技能,研究人员即使他们没有编程技能,也可以轻松地自定义经验漫步者。此外,我们通过一项使用经验摄影者进行的社会心理学研究的结果(n = 168)评估了体验摄影应用程序的实用性。平均响应率平均为84%,中位响应潜伏期为9分钟。综上所述,体验采样器创建了具有成本效益的智能手机应用程序,研究人员可以轻松地对其进行自定义,以检查日常生活中的体验。

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