PDF(Pubmed)
Abstract：
Single-case experimental design (SCED) data can be analyzed following different approaches. One of the first historically proposed options is randomizations tests, benefiting from the inclusion of randomization in the design: a desirable methodological feature. Randomization tests have become more feasible with the availability of computational resources, and such tests have been proposed for all major types of SCEDs: multiple-baseline, reversal/withdrawal, alternating treatments, and changing criterion designs. The focus of the current text is on the last of these, given that they have not been the subject of any previous simulation study. Specifically, we estimate type I error rates and statistical power for two different randomization procedures applicable to changing criterion designs: the phase change moment randomization and the blocked alternating criterion randomization. We include different series lengths, number of phases, levels of autocorrelation, and random variability. The results suggest that type I error rates are generally controlled and that sufficient power can be achieved with as few as 28-30 measurements for independent data, although more measurements are needed in case of positive autocorrelation. The presence of a reversal to a previous criterion level is beneficial. R code is provided for carrying out randomization tests following the two randomization procedures.
摘要：
可以按照不同的方法分析单案例实验设计(SCED)数据。历史上最早提出的选择之一是随机化测试，受益于在设计中包含随机化：一个理想的方法特征。随着计算资源的可用性，随机化测试变得更加可行，并且已经针对所有主要类型的SCED提出了这样的测试：多基线，逆转/撤回，交替治疗,和改变标准设计。当前文本的重点是最后一个，考虑到它们不是以前任何模拟研究的主题。具体来说,我们估计适用于改变准则设计的两种不同随机化程序的I型错误率和统计能力:相变时刻随机化和阻塞交替准则随机化.我们包括不同的系列长度，相数,自相关水平，和随机可变性。结果表明，I型错误率通常得到控制，并且可以通过少至28-30个独立数据的测量来实现足够的功率。尽管在正自相关的情况下需要更多的测量。对先前标准水平的反转的存在是有益的。提供R代码用于在两个随机化程序之后执行随机化测试。
