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Introduction to Individual and Aggregate Data Network Models for Understanding Within-Person Processes (Online)

March 22 @ 12:30 pm - 4:00 pm

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This 2-day course (3/21 & 3/22) will be offered ONLINE. It will not be recorded as there are in-class activities.

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.

Instructor: Sandra Lee and Kathleen Gates

Sandra Lee obtained a PhD in Quantitative Psychology at the University of North Carolina Chapel Hill. Prior to her PhD, she earned an SM in public health from Harvard School of Public Health with a focus on epidemiology and the social determinants of health. In-between her PhD and Master’s degree, she worked for a decade in various public health analyst roles conducting applied quantitative and qualitative research aimed at informing public health policy, practice and intervention evaluations. During this time, in addition to conducting traditional health services research and evaluation, she garnered experience in conducting community-based participatory research and formative evaluation. Her quantitative research interests and primary expertise are in the application of longitudinal and time series methods for studying within-person processes and within-person change that may aid practitioners in developing and implementing personalized prevention, treatment and intervention regimens.

Kathleen Gates is an associate professor of Quantitative Psychology  in the Department of Psychology at the University of North Carolina at Chapel Hill. She is a member of the Human Neuroimaging Group  and affiliated faculty of the UNC Biomedical Research Imaging Center (BRIC). She obtained her Ph.D. in the Department of Human Development and Family Studies (quant focus) at Penn State, a Masters of Forensic Psychology at the City University of New York (John Jay College), and a BS in Psychology from Michigan State University. Katie’s work is motivated by problems in analyzing individual-level data. She develops algorithms and programs that may aid researchers in better quantifying behavioral, psychophysiological, and emotional processes across time.

Registration fees:

  • UNC Chapel Hill Students: $0, with a $35 deposit to hold your spot (deposit is refundable upon your attendance for at least 66% of the course)
  • UNC Chapel Hill Faculty/Staff/Postdoc/Resident/Visiting Scholars: $80
  • University (Non UNC Chapel Hill) Student/Employee (must have active university email): $105
  • Government/Non-Profit/Corporate: $130

Additional course information:

  • Registration will close at 12:01am 3/18/2024. No late registrations will be accepted.
  • Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within the 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
  • Zoom link will be sent prior to the course. Registration must be made at least 3 days prior to the course date to receive the Zoom link.


For questions regarding this class, please contact Jill Stevens at