Accounting for auto-dependency in binary dyadic time series data: A comparison of model- and permutation-based approaches for testing pairwise associations.

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.