Accounting for auto-dependency in binary dyadic time series data: A comparison of model- and permutation-based approaches for testing pairwise associations.
association measures
binary data
dyadic data
model-based test
segment shuffling test
sequential analysis
significance testing
time series
Journal
The British journal of mathematical and statistical psychology
ISSN: 2044-8317
Titre abrégé: Br J Math Stat Psychol
Pays: England
ID NLM: 0004047
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
pubmed:
24
11
2020
medline:
5
11
2021
entrez:
23
11
2020
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
86-109Informations de copyright
© 2020 The British Psychological Society.
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