title: Dynamical correlation: A new method for quantifying synchrony with multivariate intensive longitudinal data.
abstract: In this article, we introduce dynamical correlation, a new method for quantifying synchrony between 2 variables with intensive longitudinal data. Dynamical correlation is a functional data analysis technique developed to measure the similarity of 2 curves. It has advantages over existing methods for studying synchrony, such as multilevel modeling. In particular, it is a nonparametric approach that does not require a prespecified functional form, and it places no assumption on homogeneity of the sample. Dynamical correlation can be easily estimated with irregularly spaced observations and tested to draw population-level inferences. We illustrate this flexible statistical technique with a simulation example and empirical data from an experiment examining interpersonal physiological synchrony between romantic partners. We discuss the advantages and limitations of the method, and how it can be extended and applied in psychological research. We also provide a set of R code for other researchers to estimate and test for dynamical correlation. (PsycINFO Database Record
authors: Department of Human Ecology.
authors: Siwei
authors: S
authors: Liu
authors: Department of Statistics, University of California.
authors: Yang
authors: Y
authors: Zhou
authors: Bouvé College of Health Sciences, Northeastern University.
authors: Richard
authors: R
authors: Palumbo
authors: Department of Statistics, University of California.
authors: Jane-Ling
authors: JL
authors: Wang
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis