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一种模块化、可扩展的方法,用于大规模生态有效行为数据。

A modular, extensible approach to massive ecologically valid behavioral data.

机构信息

Speech & Hearing Sciences, Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA.

Computer Science, School of Engineering and Applied Science, Gonzaga University, Spokane, WA, USA.

出版信息

Behav Res Methods. 2019 Aug;51(4):1754-1765. doi: 10.3758/s13428-018-1167-8.

Abstract

We explore here the application of modern computer hardware and software to the collection and analysis of behavioral data. We discuss the issues of ecological validity, storage and processing, data permanence, automation, validity, and algorithmic determinism. Taking the modern landscape into account, we demonstrate several varying projects we have recently undertaken as proofs of concept of the viability and utility of this approach. In particular, we describe four research projects, which involve work on child-directed speech; the application of automatic methods to clinical populations, including children with hearing loss; quality control and the assessment of validity; and the sharing of data in a public database. We conclude by pointing out how the methodology described here can be extended to a wide variety of interdisciplinary and detailed projects that are likely to lead to better science and improved outcomes for populations served by the behavioral, social, and health sciences.

摘要

我们在这里探讨现代计算机硬件和软件在行为数据收集和分析中的应用。我们讨论了生态有效性、存储和处理、数据持久性、自动化、有效性和算法确定性等问题。考虑到现代景观,我们展示了我们最近承担的几个不同项目,作为这种方法可行性和实用性的概念验证。特别是,我们描述了四个研究项目,涉及儿童导向语言的工作;自动方法在包括听力损失儿童在内的临床人群中的应用;质量控制和有效性评估;以及在公共数据库中共享数据。最后,我们指出了如何将这里描述的方法扩展到各种跨学科和详细的项目,这可能会导致更好的科学和改善行为、社会和健康科学服务人群的结果。

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