Capturing fluctuations in multivariate intensive longitudinal data

Abstract

This paper introduces a novel method for intensive longitudinal data incorporating dimension reduction and time series analyses. The method capitalizes on the notion of determining distance or similarity parameters in the data. The method is a three-phase approach where, 1) distance parameters are determined for each individual, 2) optimal distances between the variables are computed across all participant and time points, and 3) a one-dimensional solution is computed across all time-points for each participant. A first-order autoregressive model was fit to each individual's solution vector to examine intra-individual dynamics and allow for comparisons of inter-individual trajectories. The method constructs a one-dimensional representation at each time-point while preserving the structure of the relationships between variables.