## Overview

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.

## Outline

- Prepare simulation of time-series data
- Model time-series data with uSEM
- Post-processing of LISREL output
- Plot network graph
- Calculate network metrics
- Demonstrate network metrics

## Introduction

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 individualâ€™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.