Friedel Jonathan E, Cox Alison, Galizio Ann, Swisher Melissa, Small Megan L, Perez Sofia
Department of Psychology, Georgia Southern University, 2670 Southern Drive, Statesboro, GA 30460-8041 USA.
Brock University, St. Catharines, ON Canada.
Perspect Behav Sci. 2021 Nov 24;45(1):209-237. doi: 10.1007/s40614-021-00318-7. eCollection 2022 Mar.
Group-based experimental designs are an outgrowth of the logic of null-hypothesis significance testing and thus, statistical tests are often considered inappropriate for single-case experimental designs. Behavior analysts have recently been more supportive of efforts to include appropriate statistical analysis techniques to evaluate single-case experimental design data. One way that behavior analysts can incorporate statistical analyses into their practices with single-case experimental designs is to use Monte Carlo analyses. These analyses compare experimentally obtained behavioral data to simulated samples of behavioral data to determine the likelihood that the experimentally obtained results occurred due to chance (i.e., a value). Monte Carlo analyses are more in line with behavior analytic principles than traditional null-hypothesis significance testing. We present an open-source Monte Carlo tool, created in for behavior analysts who want to use Monte Carlo analyses in addition as part of their data analysis.
基于组的实验设计是虚无假设显著性检验逻辑的产物，因此，统计检验通常被认为不适用于单被试实验设计。行为分析师最近更支持采用适当的统计分析技术来评估单被试实验设计数据的努力。行为分析师将统计分析纳入其单被试实验设计实践的一种方法是使用蒙特卡洛分析。这些分析将实验获得的行为数据与行为数据的模拟样本进行比较，以确定实验获得的结果是由于偶然因素（即p值）而出现的可能性。与传统的虚无假设显著性检验相比，蒙特卡洛分析更符合行为分析原则。我们为希望将蒙特卡洛分析作为其数据分析一部分的行为分析师展示一个在R中创建的开源蒙特卡洛工具。
