Events

Event Title	Introduction to Individual and Aggregate Data Network Models for Understanding Within-Person Processes (Online)
Location	Online via Zoom
Sponsor	H.W. Odum Institute
Date/Time	10/30/2024 - 10/31/2024 12:30 PM - 4:00 PM
Event Price
Cutoff Date	10/28/2024 Must register before this date

For more information, contact the event administrator: Jill Stevens jill_stevens@unc.edu

Event Presenters

Name	Title
Sandra Lee, PhD	Statistical Consultant at HW Odum Institute, UNC Chapel Hill
Kathleen Gates, PhD	Associate professor of Quantitative Psychology in the Department of Psychology at UNC Chapel Hill.

This 2-day course (10/30 & 10/31) will be offered ONLINE. It will not be recorded as there are in-class activities.

Description:
Using network models to understand human processes

With increased interest in person-centered interventions and treatments has come increased interest in understanding human processes as they unfold within individuals. Additionally, technological advances have made the collection of person-specific data easier and more cost-effective for researchers interested in studying human behavior within everyday contexts. This two-day course focuses on using two popular network models to explore research questions concerning within-person processes.

This course is intended for individuals with research questions that can be answered using multivariate time series data/intensive longitudinal data. Examples of such data include daily diary data; data collected via self-report through ecological momentary sampling (ESM); passive data from cell phones; and other psychophysiological data such as MRI data or heart rate data.

The two network modeling frameworks presented in this course are graphicalVAR (GVAR) and Group Iterative Multiple Model Estimation (GIMME). Both models can be used to explore processes as they unfold within individuals to obtain individual person-specific network models (idiographic analysis) or group/population level network models (nomothetic analysis). Differences between the modeling frameworks will be presented. Challenges and considerations for choosing between methods will be discussed.

Statistical models and topics presented during this course include the following:

Vector autoregression (VAR) time series analysis in both non-SEM and SEM frameworks
Multilevel Modeling (MLM)
Issues with aggregating data when trying to understand within-person processes
Fitting of network models using graphicalVAR and GIMME approaches
Approaches for summarizing idiographic results using meta-analytic techniques
This course is applied in nature and offers a conceptual overview of the modeling frameworks as well as the technical details needed to fit the models. Examples with hands on learning will be provided throughout the two-day course to give learners the opportunity to practice fitting and interpreting models.