This tutorial introduces a hybrid method that combines intraindividual variability methods and network analysis methods in order to model individuals as high-dimensional dynamic systems. This hybrid method is designed and tested to quantify the extent of interaction in a high-dimensional multivariate system, and applicable on experience sampling data.

This turorial corresponds to the following paper (under review): Yang, X., Ram, N., Gest, S. D., Lydon, D. M., Conroy, D. E., Pincus, A. L., Molenaar, P. M. C.. Individuals as Dynamic Networks That Change: Merging Intraindividual Variability, Network Analysis, and Experience Sampling. Journal of Gerontology, Series B: Psychological Sciences and Social Science.


  1. Prepare simulation of time-series data
  2. Model time-series data with uSEM
  3. Post-processing of LISREL output
  4. Plot network graph
  5. Calculate network metrics
  6. Demonstrate network metrics


Lifespan developmental theories view persons as dynamic systems, with feelings, thoughts and actions that are interconnected and change over time. In order to understand this aspect of individual’s psychological functioning, we need repeatedly measured multivariate psychological data, as well as methods to quantify the interrelations among variables. Based upon the interrelations, we can examine the long-term change of the interrelations by applying network analysis methods.

In the paper aforementioned, we forward an approach that merges intraindividual variability methods, network analysis methods, and measurement-burst designs in order to describe the interplay among many aspects of functioning and the change in this interplay over time.

uSEM (unified Structural Equation Modeling, Beltz et al., 2013; Gates et al., 2010; Kim et al., 2007) incorporates time-lagged and contemporaneous relations in one model. uSEM utilizes the intraindividual variablity and is caplable of modeling high-dimensional time-series data.