Natesan Batley Prathiba, Nandakumar Ratna, Palka Jayme M, Shrestha Pragya
College of Health and Life Sciences, Brunel University London, Uxbridge, United Kingdom.
School of Education, University of Delaware, Newark, DE, United States.
Front Psychol. 2021 Jan 15;11:617047. doi: 10.3389/fpsyg.2020.617047. eCollection 2020.
Recently, there has been an increased interest in developing statistical methodologies for analyzing single case experimental design (SCED) data to supplement visual analysis. Some of these are simulation-driven such as Bayesian methods because Bayesian methods can compensate for small sample sizes, which is a main challenge of SCEDs. Two simulation-driven approaches: Bayesian unknown change-point model (BUCP) and simulation modeling analysis (SMA) were compared in the present study for three real datasets that exhibit "clear" immediacy, "unclear" immediacy, and delayed effects. Although SMA estimates can be used to answer some aspects of functional relationship between the independent and the outcome variables, they cannot address immediacy or provide an effect size estimate that considers autocorrelation as required by the What Works Clearinghouse (WWC) Standards. BUCP overcomes these drawbacks of SMA. In final analysis, it is recommended that both visual and statistical analyses be conducted for a thorough analysis of SCEDs.
最近，人们对开发用于分析单病例实验设计（SCED）数据以补充视觉分析的统计方法越来越感兴趣。其中一些方法是由模拟驱动的，比如贝叶斯方法，因为贝叶斯方法可以弥补小样本量的问题，而小样本量是单病例实验设计的主要挑战之一。在本研究中，针对三个呈现出“明显”即时性、“不明显”即时性和延迟效应的真实数据集，对两种由模拟驱动的方法：贝叶斯未知变化点模型（BUCP）和模拟建模分析（SMA）进行了比较。虽然模拟建模分析的估计值可用于回答自变量与结果变量之间函数关系的某些方面，但它们无法解决即时性问题，也无法提供像有效证据交流中心（WWC）标准所要求的那样考虑自相关的效应量估计。贝叶斯未知变化点模型克服了模拟建模分析的这些缺点。归根结底，建议同时进行视觉分析和统计分析，以便对单病例实验设计进行全面分析。
