SUNY Upstate Medical University.
University of Nebraska Medical Center's Munroe-Meyer Institute.
J Exp Anal Behav. 2019 Mar;111(2):309-328. doi: 10.1002/jeab.500. Epub 2019 Feb 1.
Randomization statistics offer alternatives to many of the statistical methods commonly used in behavior analysis and the psychological sciences, more generally. These methods are more flexible than conventional parametric and nonparametric statistical techniques in that they make no assumptions about the underlying distribution of outcome variables, are relatively robust when applied to small-n data sets, and are generally applicable to between-groups, within-subjects, mixed, and single-case research designs. In the present article, we first will provide a historical overview of randomization methods. Next, we will discuss the properties of randomization statistics that may make them particularly well suited for analysis of behavior-analytic data. We will introduce readers to the major assumptions that undergird randomization methods, as well as some practical and computational considerations for their application. Finally, we will demonstrate how randomization statistics may be calculated for mixed and single-case research designs. Throughout, we will direct readers toward resources that they may find useful in developing randomization tests for their own data.
随机化统计为行为分析和更广泛的心理学科学中常用的许多统计方法提供了替代方案。这些方法比传统的参数和非参数统计技术更灵活，因为它们不对结果变量的基础分布做出假设，在应用于小样本数据集时相对稳健，并且通常适用于组间、个体内、混合和单案例研究设计。在本文中，我们首先将提供随机化方法的历史概述。接下来，我们将讨论随机化统计的特性，这些特性可能使它们特别适合分析行为分析数据。我们将向读者介绍支持随机化方法的主要假设，以及它们应用的一些实际和计算方面的考虑。最后，我们将展示如何为混合和单案例研究设计计算随机化统计。在整个过程中，我们将引导读者了解他们可能在为自己的数据开发随机检验时有用的资源。
