"Dynamical correlation: A new method for quantifying synchrony with multivariate intensive longitudinal data": Correction to Liu et al. (2016).

Reports an error in "Dynamical correlation: A new method for quantifying synchrony with multivariate intensive longitudinal data" by Siwei Liu, Yang Zhou, Richard Palumbo and Jane-Ling Wang ( Psychological Methods , 2016[Sep], Vol 21[3], 291-308). In the article, there were errors in the R script of Appendix B which could lead to incorrect significance testing results for dynamical correlation. We created an updated R script with corrections. In the updated R script, argument "na" from function "ind_DC" and argument "ms" from function "boot_test_DC" were removed. Codes to check if there is any missing value in the data, and to compute proportion of missing values in the data were added. A warning was added when too many missing values are present. In addition, argument 't' is now correctly labeled "a vector of time points where x,y are observed". The updated R script with corrections can be downloaded from the first author's personal website: https://siweiliu.weebly.com/publications.html. (The following abstract of the original article appeared in record 2016-07276-001.) 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