Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine.
J Appl Behav Anal. 2020 Apr;53(2):1177-1191. doi: 10.1002/jaba.689. Epub 2020 Feb 12.
Current methods employed to interpret functional analysis data include visual analysis and post-hoc visual inspection (PHVI). However, these methods may be biased by dataset complexity, hand calculations, and rater experience. We examined whether an automated approach using nonparametric rank-based statistics could increase the accuracy and efficiency of functional analysis data interpretation. We applied Automated Nonparametric Statistical Analysis (ANSA) to a sample of 65 published functional analyses for which additional experimental evidence was available to verify behavior function. Results showed that exact behavior function agreement between ANSA and the publications authors was 83.1%, exact agreement between ANSA and PHVI was 75.4%, and exact agreement across all 3 methods was 64.6%. These preliminary findings suggest that ANSA has the potential to support the data interpretation process. A web application that incorporates the calculations and rules utilized by ANSA is accessible at https://ansa.shinyapps.io/ansa/.
目前用于解释功能分析数据的方法包括视觉分析和事后视觉检查（PHVI）。然而，这些方法可能会受到数据集复杂性、手工计算和评分者经验的影响。我们研究了使用基于非参数等级的统计的自动化方法是否可以提高功能分析数据解释的准确性和效率。我们将非参数统计分析自动化（ANSA）应用于 65 个已发表的功能分析的样本，这些样本有额外的实验证据来验证行为功能。结果表明，ANSA 与出版物作者之间的精确行为功能一致性为 83.1%，ANSA 与 PHVI 之间的精确一致性为 75.4%，而所有 3 种方法之间的精确一致性为 64.6%。这些初步结果表明，ANSA 有可能支持数据解释过程。一个包含 ANSA 所使用的计算和规则的网络应用程序可在 https://ansa.shinyapps.io/ansa/ 上获得。
