Many theories have been put forward on how people become synchronized or co-regulate each other in daily interactions. These theories are often tested by observing a dyad and coding the presence of multiple target behaviours in small time intervals. The sequencing and co-occurrence of the partners’ behaviours across time are then quantified by means of association measures (e.g., kappa coefficient, Jaccard similarity index, proportion of agreement). We demonstrate that the association values obtained are not easy to interpret, because they depend on the marginal frequencies and the amount of auto-dependency in the data. Moreover, often no inferential framework is available to test the significance of the association. Even if a significance test exists (e.g., kappa coefficient) auto-dependencies are not taken into account, which, as we will show, can seriously inflate the Type I error rate. We compare the effectiveness of a model- and a permutation-based framework for significance testing. Results of two simulation studies show that within both frameworks test variants exist that successfully account for auto-dependency, as the Type I error rate is under control, while power is good.

中文翻译：

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考虑二进制二元时间序列数据中的自动依赖性：基于模型和排列的方法的比较，用于测试成对关联

关于人们如何在日常互动中变得同步或相互调节，已经提出了许多理论。这些理论通常通过观察二元组并在小时间间隔内编码多个目标行为的存在来进行测试。然后通过关联度量（例如，kappa 系数、Jaccard 相似性指数、一致性比例）来量化合作伙伴行为在时间上的排序和共同发生。我们证明获得的关联值不容易解释，因为它们取决于边际频率和数据中的自依赖性量。此外，通常没有推理框架可用于测试关联的重要性。即使存在显着性检验（例如，kappa 系数），也不会考虑自相关性，正如我们将展示的那样，会严重夸大 I 类错误率。我们比较了基于模型和基于排列的框架进行显着性检验的有效性。两项模拟研究的结果表明，在两个框架内都存在成功解释自依赖性的测试变体，因为 I 类错误率得到控制，而功效良好。