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We introduce dynamical correlation, a new statistical method to measure synchrony in intensive longitudinal data. This flexible approach quantifies curve similarity, offering advantages over traditional methods for psychological research.

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Area of Science:

  • Statistics
  • Functional Data Analysis
  • Psychology

Background:

  • Quantifying synchrony in intensive longitudinal data is crucial for understanding dynamic processes.
  • Existing methods like multilevel modeling have limitations, including assumptions of functional form and sample homogeneity.

Purpose of the Study:

  • Introduce dynamical correlation, a novel nonparametric method for quantifying synchrony between two variables.
  • Demonstrate its flexibility and advantages over existing techniques for analyzing intensive longitudinal data.

Main Methods:

  • Dynamical correlation is a functional data analysis technique measuring curve similarity.
  • It is nonparametric, handles irregularly spaced observations, and allows for population-level inferences.
  • The method was illustrated using simulation and empirical data on interpersonal physiological synchrony.

Main Results:

  • Dynamical correlation effectively quantifies synchrony between variables with intensive longitudinal data.
  • The method is flexible, adaptable to irregularly spaced data, and does not assume homogeneity.
  • Empirical data analysis demonstrated its utility in examining interpersonal physiological synchrony.

Conclusions:

  • Dynamical correlation offers a powerful and flexible tool for analyzing synchrony in psychological research.
  • The method's nonparametric nature and ability to handle complex data structures provide significant advantages.
  • R code is provided to facilitate the adoption and application of dynamical correlation by researchers.